Analisi_Temporal_Afiliacions_Rubí_Sectors.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "19b7b4b9",
   "metadata": {},
   "source": [
    "Analalis de série temporal del data set de Afiliaciones por sectores de Rubí"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "fae55d31",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "e5c2096b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cargar los datos\n",
    "datos = pd.read_excel(\"./Downloads/Afiliacions_Exemple_Mitjanes_Anuals.xlsx\", header = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "a4b2738c",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Agricultura</th>\n",
       "      <th>Indústria</th>\n",
       "      <th>Construcció</th>\n",
       "      <th>Serveis</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023-08-01</td>\n",
       "      <td>70</td>\n",
       "      <td>7145</td>\n",
       "      <td>2405</td>\n",
       "      <td>26350</td>\n",
       "      <td>35970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-07-01</td>\n",
       "      <td>70</td>\n",
       "      <td>7170</td>\n",
       "      <td>2430</td>\n",
       "      <td>26595</td>\n",
       "      <td>36260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-06-01</td>\n",
       "      <td>70</td>\n",
       "      <td>7185</td>\n",
       "      <td>2450</td>\n",
       "      <td>26720</td>\n",
       "      <td>36425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-05-01</td>\n",
       "      <td>65</td>\n",
       "      <td>7180</td>\n",
       "      <td>2435</td>\n",
       "      <td>26975</td>\n",
       "      <td>36660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-04-01</td>\n",
       "      <td>65</td>\n",
       "      <td>7170</td>\n",
       "      <td>2440</td>\n",
       "      <td>27005</td>\n",
       "      <td>36675</td>\n",
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      ],
      "text/plain": [
       "  Unnamed: 0  Agricultura  Indústria  Construcció  Serveis  Total\n",
       "0 2023-08-01           70       7145         2405    26350  35970\n",
       "1 2023-07-01           70       7170         2430    26595  36260\n",
       "2 2023-06-01           70       7185         2450    26720  36425\n",
       "3 2023-05-01           65       7180         2435    26975  36660\n",
       "4 2023-04-01           65       7170         2440    27005  36675"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datos.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "b822596d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cambiar el nombre de la columna 'Unnamed: 0' a 'Fecha'\n",
    "datos.rename(columns={'Unnamed: 0': 'Fecha'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "94ca4824",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fecha</th>\n",
       "      <th>Agricultura</th>\n",
       "      <th>Indústria</th>\n",
       "      <th>Construcció</th>\n",
       "      <th>Serveis</th>\n",
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       "      <th>0</th>\n",
       "      <td>2023-08-01</td>\n",
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       "      <td>7145</td>\n",
       "      <td>2405</td>\n",
       "      <td>26350</td>\n",
       "      <td>35970</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-07-01</td>\n",
       "      <td>70</td>\n",
       "      <td>7170</td>\n",
       "      <td>2430</td>\n",
       "      <td>26595</td>\n",
       "      <td>36260</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-06-01</td>\n",
       "      <td>70</td>\n",
       "      <td>7185</td>\n",
       "      <td>2450</td>\n",
       "      <td>26720</td>\n",
       "      <td>36425</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-05-01</td>\n",
       "      <td>65</td>\n",
       "      <td>7180</td>\n",
       "      <td>2435</td>\n",
       "      <td>26975</td>\n",
       "      <td>36660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-04-01</td>\n",
       "      <td>65</td>\n",
       "      <td>7170</td>\n",
       "      <td>2440</td>\n",
       "      <td>27005</td>\n",
       "      <td>36675</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>2013-03-01</td>\n",
       "      <td>60</td>\n",
       "      <td>6631</td>\n",
       "      <td>1833</td>\n",
       "      <td>20336</td>\n",
       "      <td>28860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>55</td>\n",
       "      <td>6676</td>\n",
       "      <td>1896</td>\n",
       "      <td>20406</td>\n",
       "      <td>29033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>2012-09-01</td>\n",
       "      <td>74</td>\n",
       "      <td>6843</td>\n",
       "      <td>1958</td>\n",
       "      <td>20706</td>\n",
       "      <td>29581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>2012-06-01</td>\n",
       "      <td>76</td>\n",
       "      <td>6948</td>\n",
       "      <td>2023</td>\n",
       "      <td>20542</td>\n",
       "      <td>29589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>2012-03-01</td>\n",
       "      <td>72</td>\n",
       "      <td>6968</td>\n",
       "      <td>2045</td>\n",
       "      <td>20494</td>\n",
       "      <td>29579</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73 rows × 6 columns</p>\n",
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      ],
      "text/plain": [
       "        Fecha  Agricultura  Indústria  Construcció  Serveis  Total\n",
       "0  2023-08-01           70       7145         2405    26350  35970\n",
       "1  2023-07-01           70       7170         2430    26595  36260\n",
       "2  2023-06-01           70       7185         2450    26720  36425\n",
       "3  2023-05-01           65       7180         2435    26975  36660\n",
       "4  2023-04-01           65       7170         2440    27005  36675\n",
       "..        ...          ...        ...          ...      ...    ...\n",
       "68 2013-03-01           60       6631         1833    20336  28860\n",
       "69 2012-12-01           55       6676         1896    20406  29033\n",
       "70 2012-09-01           74       6843         1958    20706  29581\n",
       "71 2012-06-01           76       6948         2023    20542  29589\n",
       "72 2012-03-01           72       6968         2045    20494  29579\n",
       "\n",
       "[73 rows x 6 columns]"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datos.head(73)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "0628753f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extraer el año y crear una nueva columna 'Año'\n",
    "datos['Año'] = datos['Fecha'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "ab44514b",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Fecha</th>\n",
       "      <th>Agricultura</th>\n",
       "      <th>Indústria</th>\n",
       "      <th>Construcció</th>\n",
       "      <th>Serveis</th>\n",
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       "      <td>7145</td>\n",
       "      <td>2405</td>\n",
       "      <td>26350</td>\n",
       "      <td>35970</td>\n",
       "      <td>2023</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-07-01</td>\n",
       "      <td>70</td>\n",
       "      <td>7170</td>\n",
       "      <td>2430</td>\n",
       "      <td>26595</td>\n",
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       "      <th>2</th>\n",
       "      <td>2023-06-01</td>\n",
       "      <td>70</td>\n",
       "      <td>7185</td>\n",
       "      <td>2450</td>\n",
       "      <td>26720</td>\n",
       "      <td>36425</td>\n",
       "      <td>2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-05-01</td>\n",
       "      <td>65</td>\n",
       "      <td>7180</td>\n",
       "      <td>2435</td>\n",
       "      <td>26975</td>\n",
       "      <td>36660</td>\n",
       "      <td>2023</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-04-01</td>\n",
       "      <td>65</td>\n",
       "      <td>7170</td>\n",
       "      <td>2440</td>\n",
       "      <td>27005</td>\n",
       "      <td>36675</td>\n",
       "      <td>2023</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Fecha  Agricultura  Indústria  Construcció  Serveis  Total   Año\n",
       "0 2023-08-01           70       7145         2405    26350  35970  2023\n",
       "1 2023-07-01           70       7170         2430    26595  36260  2023\n",
       "2 2023-06-01           70       7185         2450    26720  36425  2023\n",
       "3 2023-05-01           65       7180         2435    26975  36660  2023\n",
       "4 2023-04-01           65       7170         2440    27005  36675  2023"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datos.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e710e193",
   "metadata": {},
   "source": [
    "# Visualización de las series temporales "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "2412604a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Crea un gráfico de serie temporal para cada sector\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(datos['Año'], datos['Agricultura'], label='Agricultura')\n",
    "plt.plot(datos['Año'], datos['Indústria'], label='Indústria')\n",
    "plt.plot(datos['Año'], datos['Construcció'], label='Construcció')\n",
    "plt.plot(datos['Año'], datos['Serveis'], label='Serveis')\n",
    "plt.plot(datos['Año'], datos['Total'], label='Total')\n",
    "\n",
    "# Añade etiquetas y título\n",
    "plt.xlabel('Año')\n",
    "plt.ylabel('Media Anual')\n",
    "plt.title('Afiliacions a la Seguretat Social a Rubí')\n",
    "\n",
    "# Muestra la leyenda\n",
    "plt.legend()\n",
    "\n",
    "# Muestra el gráfico\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e660265",
   "metadata": {},
   "source": [
    "## 1. Visualización de la Tendencia:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "afa9f51e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Visualizar la tendencia del total\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(datos['Año'], datos['Total'], label = 'Total', marker='o', linestyle='-')\n",
    "plt.title('Tendencia a lo largo del Tiempo')\n",
    "plt.xlabel('Año')\n",
    "plt.ylabel('Total')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f862e5c",
   "metadata": {},
   "source": [
    "## 2. Suavización de la Tendencia con Media Móvil:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "c5d90bc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calcular la media móvil\n",
    "datos['Media Móvil'] = datos['Total'].rolling(window=3).mean()  # Puedes ajustar el tamaño de la ventana\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "b77a446c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizar la tendencia suavizada\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(datos['Año'], datos['Media Móvil'], marker='', linestyle='-', color='orange', label='Media Móvil')\n",
    "plt.title('Tendencia Suavizada a lo largo del Tiempo')\n",
    "plt.xlabel('Año')\n",
    "plt.ylabel('Total')\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d5e5c00",
   "metadata": {},
   "source": [
    "## Modelado de la tendencia:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "c3b77144",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scikit-learn in c:\\users\\regidor\\anaconda3\\lib\\site-packages (1.2.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\regidor\\anaconda3\\lib\\site-packages (from scikit-learn) (2.2.0)\n",
      "Requirement already satisfied: numpy>=1.17.3 in c:\\users\\regidor\\anaconda3\\lib\\site-packages (from scikit-learn) (1.23.5)\n",
      "Requirement already satisfied: joblib>=1.1.1 in c:\\users\\regidor\\anaconda3\\lib\\site-packages (from scikit-learn) (1.1.1)\n",
      "Requirement already satisfied: scipy>=1.3.2 in c:\\users\\regidor\\anaconda3\\lib\\site-packages (from scikit-learn) (1.10.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "e9262393",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# Preparar los datos para el modelo de regresión\n",
    "X = datos['Año'].values.reshape(-1, 1)  # Años\n",
    "y = datos['Total'].values  # Totales\n",
    "\n",
    "# Crear y entrenar el modelo de regresión lineal\n",
    "modelo = LinearRegression()\n",
    "modelo.fit(X, y)\n",
    "\n",
    "# Predecir los valores\n",
    "predicciones = modelo.predict(X)\n",
    "\n",
    "# Establecer el estilo de Seaborn\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "# Graficar los datos originales y la tendencia modelada\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(datos['Año'], datos['Total'], marker='o', linestyle='-', color='blue', label='Datos originales')\n",
    "plt.plot(datos['Año'], predicciones, color='red', label='Tendencia modelada')\n",
    "\n",
    "# Configurar el título y las etiquetas\n",
    "plt.title('Modelado de la Tendencia a lo Largo del Tiempo')\n",
    "plt.xlabel('Año')\n",
    "plt.ylabel('Total')\n",
    "plt.grid(True)\n",
    "\n",
    "# Mostrar la leyenda\n",
    "plt.legend()\n",
    "\n",
    "# Mostrar el gráfico\n",
    "plt.show()\n"
   ]
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   "cell_type": "code",
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   "id": "39b0ab5e",
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     "text": [
      "La ecuación de la línea de tendencia es y = 672.01x + -1323059.50\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# Preparar los datos para el modelo de regresión\n",
    "X = datos['Año'].values.reshape(-1, 1)  # Años\n",
    "y = datos['Total'].values  # Totales\n",
    "\n",
    "# Crear y entrenar el modelo de regresión lineal\n",
    "modelo = LinearRegression()\n",
    "modelo.fit(X, y)\n",
    "\n",
    "# Obtener la pendiente (m) y la intersección y (b)\n",
    "m = modelo.coef_[0]\n",
    "b = modelo.intercept_\n",
    "\n",
    "print(f\"La ecuación de la línea de tendencia es y = {m:.2f}x + {b:.2f}\")\n"
   ]
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "# Preparar los datos para el modelo de regresión\n",
    "X = datos['Año'].values.reshape(-1, 1)  \n",
    "y = datos['Total'].values  \n",
    "\n",
    "# Crear y entrenar el modelo de regresión lineal\n",
    "modelo = LinearRegression()\n",
    "modelo.fit(X, y)\n",
    "\n",
    "# Predecir los valores\n",
    "predicciones = modelo.predict(X)\n",
    "\n",
    "# Crear el gráfico\n",
    "trace1 = go.Scatter(x=datos['Año'], y=datos['Total'], mode='lines+markers', name='Datos originales')\n",
    "trace2 = go.Scatter(x=datos['Año'], y=predicciones, mode='lines', name='Tendencia modelada')\n",
    "\n",
    "layout = go.Layout(\n",
    "    title='Modelado de la Tendencia a lo Largo del Tiempo',\n",
    "    xaxis=dict(title='Año'),\n",
    "    yaxis=dict(title='Total'),\n",
    "    showlegend=True\n",
    ")\n",
    "\n",
    "fig = go.Figure(data=[trace1, trace2], layout=layout)\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac0fecf8",
   "metadata": {},
   "source": [
    "## Estacionalidad:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "30028009",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: statsmodels in c:\\users\\regidor\\anaconda3\\lib\\site-packages (0.13.5)\n",
      "Requirement already satisfied: scipy>=1.3 in c:\\users\\regidor\\anaconda3\\lib\\site-packages (from statsmodels) (1.10.0)\n",
      "Requirement already satisfied: patsy>=0.5.2 in c:\\users\\regidor\\anaconda3\\lib\\site-packages (from statsmodels) (0.5.3)\n",
      "Requirement already satisfied: packaging>=21.3 in c:\\users\\regidor\\anaconda3\\lib\\site-packages (from statsmodels) (22.0)\n",
      "Requirement already satisfied: pandas>=0.25 in c:\\users\\regidor\\anaconda3\\lib\\site-packages (from statsmodels) (1.5.3)\n",
      "Requirement already satisfied: numpy>=1.22.3 in c:\\users\\regidor\\anaconda3\\lib\\site-packages (from statsmodels) (1.23.5)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in c:\\users\\regidor\\anaconda3\\lib\\site-packages (from pandas>=0.25->statsmodels) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\regidor\\anaconda3\\lib\\site-packages (from pandas>=0.25->statsmodels) (2022.7)\n",
      "Requirement already satisfied: six in c:\\users\\regidor\\anaconda3\\lib\\site-packages (from patsy>=0.5.2->statsmodels) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install statsmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "fadbec05",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "233aec34",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Realiza la descomposición estacional\n",
    "resultado = seasonal_decompose(datos['Total'], model='multiplicative', period = 12)  # O 'additive' dependiendo de la naturaleza de tus datos\n",
    "\n",
    "# Plotea los componentes descompuestos de la serie temporal\n",
    "resultado.plot()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "7bd54c8e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Realiza la descomposición estacional\n",
    "resultado = seasonal_decompose(datos['Total'], model='additive', period = 12)  # O 'additive' dependiendo de la naturaleza de tus datos\n",
    "\n",
    "# Plotea los componentes descompuestos de la serie temporal\n",
    "resultado.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "305a7293",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     False\n",
       "1     False\n",
       "2     False\n",
       "3     False\n",
       "4     False\n",
       "      ...  \n",
       "68    False\n",
       "69    False\n",
       "70    False\n",
       "71    False\n",
       "72    False\n",
       "Name: Total, Length: 73, dtype: bool"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "z_scores = stats.zscore(datos['Total'])\n",
    "abs_z_scores = np.abs(z_scores)\n",
    "outliers = (abs_z_scores > 3)  # Cambia 3 a tu criterio\n",
    "outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "5cd851e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series([], Name: Total, dtype: int64)\n"
     ]
    }
   ],
   "source": [
    "outliers_values = datos['Total'][outliers]\n",
    "print(outliers_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "5be6cb9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0            NaN\n",
      "1            NaN\n",
      "2            NaN\n",
      "3            NaN\n",
      "4            NaN\n",
      "         ...    \n",
      "68    840.666024\n",
      "69    750.955432\n",
      "70    646.226600\n",
      "71    506.327979\n",
      "72    418.203409\n",
      "Name: Volatilidad, Length: 73, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "datos['Volatilidad'] = datos['Total'].rolling(window=12).std()\n",
    "print(datos['Volatilidad'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "bb076c88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             Agricultura  Indústria  Construcció   Serveis\n",
      "Agricultura     1.000000   0.285389     0.347376  0.159257\n",
      "Indústria       0.285389   1.000000     0.774789  0.717201\n",
      "Construcció     0.347376   0.774789     1.000000  0.949856\n",
      "Serveis         0.159257   0.717201     0.949856  1.000000\n"
     ]
    }
   ],
   "source": [
    "correlacion = datos[['Agricultura', 'Indústria', 'Construcció', 'Serveis']].corr()\n",
    "print(correlacion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "067d300f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Gráfico de dispersión con línea de mejor ajuste para Industria vs Construcción\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.regplot(data=datos, x='Indústria', y='Construcció')\n",
    "plt.title('Correlación entre Industria y Construcción')\n",
    "plt.show()\n",
    "\n",
    "# Gráfico de dispersión con línea de mejor ajuste para Industria vs Servicios\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.regplot(data=datos, x='Indústria', y='Serveis')\n",
    "plt.title('Correlación entre Industria y Servicios')\n",
    "plt.show()\n",
    "\n",
    "# Gráfico de dispersión con línea de mejor ajuste para Construcción vs Servicios\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.regplot(data=datos, x='Construcció', y='Serveis')\n",
    "plt.title('Correlación entre Construcción y Servicios')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "d082a128",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9LG29e/e2lPNnzpxJu3btqFu3ruV8RkYGLi4uBY67wInBjBkzOHv2bJ5sBCAkJISnn36a3r17F/iFRUREbjclsf9AjRo1CAkJYdq0aUyaNIn4+Hjmz59Pz54981y7Z88e3nrrLVauXImfnx+fffYZZ8+e5bHHHgPgxIkT7N27l1mzZuHt7c2CBQtISkqiTZs2BY6nwJ/A9u3bCQwMzPdcuXLliI6OLvCLioiI3I5KaufD2bNnk5WVRevWrenduzfNmjVjyJAhAAQFBbF+/XoAOnToQJ8+fejTpw9NmjRh27ZtLFmyBF9fXwDeeust7rzzTrp27UpoaCiRkZF88skn+Pj4FPwzMN98/+QC+OKLL6weZ2Zmsm3bNpKTk1m6dKktXQGacCfWIj7SfTfkhu2f7y7pEOQ28+OGFkXaf+yk5+zWl++4BXbrqzjZPPlw9uzZVo+NRiOBgYGMHz/ebkGJiIhIybA5Mdi+fXtRxCEiIlLiSvPNj+ylwInBnj17/uc1uesoRURESiODofTe/MheCpwY5C6NuHmpore3N4mJiZhMJnx8fNi9W/VAERGR0qzAicGxY8cAWLRoESdOnODNN9/Ey8uLlJQUpk+fjre3d5EFKSIiUixUSrB9jsGiRYvYvn07rq6uwPUdm8aMGUPz5s0ZMWKE3QMUEREpLiWxj8HtxuZPwGQyERsba9V28eJFjEbjLZ4hIiIipYXNIwZdu3ZlwIABDBw4kCpVqnDhwgU++ugj3XZZRERKPa1KKERiMHLkSNzd3fnggw+Ijo6mSpUq9O7dm2effbYo4hMRESk+WpVge2Lg6OjISy+9xEsvvVQU8YiIiEgJsjkxyM7OZvPmzZw7dw6TyWR1bujQoXYLTEREpLiplFCIxGD8+PF8/fXX3HPPPTg63ni6Pe9hLSIiUiK0KsH2xOC7777j008/5YEHHiiKeEREREqM/sgt5HLFunXrFkUsIiIiUsJsTgw6derEokWLiiIWERGRkuXgYL+jlLK5lHDkyBH279/PBx98QIUKFazObdu2zW6BiYiIFDdNPixEYtCrVy969epVFLGIiIhICStwYjB37tyijENERKTkaYOjgicGERERf3teMzlFRKTUUymh4InB0qVLizIOERERuQ3YPMdARESkrDKolKDEQERExEKlBNv3MRAREZGySyMGIiIiOQyleGMie1FiICIikksr7JQYiIiIWGjEQHMMRERE5AaNGIiIiORSKUGJgYiISC5NPlQpQURERG6iEQMREZFc2vlQiYGIiIiFdj5UKUFERERu0IiBiIhIDt1ESYmBiIjIDSolqJQgIiIiN2jEQEREJJdKCUoMRERELLTzoRIDERERC+18qDkGIiIicoNGDERERHJpjoESAxEREQstV1QpQURERG7QiIGIiEgulRKUGIiIiFhouaJKCSIiInKDRgxERERyaR8DJQYiIiIWKiWolCAiIiI3aMRAREQkl1YlKDEQERGx0BwDJQYiIiIWmmNQ8olBxEeHSjoEuY2EDnygpEOQ28jQ15qUdAhy22lR0gGUeSWeGIiIiNw2NMdAiYGIiIiFSglarigiIiI3KDEQERHJ5eBgv8MGsbGxDBkyhAYNGhAaGsrUqVPJysrK99rw8HDat29PUFAQffr0Yc+ePVbnFy5cSPPmzalfvz79+vXjzJkztn0ENl0tIiJShpkNBrsdthg+fDju7u7s3LmTzz//nN27d7N48eI8123bto3x48fz+uuvs3fvXgYMGMCzzz5r+fJft24dS5cuZdGiRURERHDffffx4osvYjabCxyLEgMREZEikJGRQVJSktWRkZGR57rz588TGRnJyJEjcXNzo1q1agwZMoTly5fnufarr76iU6dOtGzZEqPRSNu2bWnQoAFr164FYPXq1fTt25datWrh4uLCiBEjiIqKIiIiosBxKzEQERHJZXCw2xEWFkZISIjVERYWluclT548iY+PD/7+/pa2wMBAoqKiuHbtmtW12dnZuLu7W7U5ODhYRgxOnTpF7dq1LeecnJyoUaMGx44dK/BHoFUJIiIiuey4XHHQoEH079/fqs3Z2TnPdcnJybi5uVm15T5OSUmhXLlylvZ27doxbtw42rVrR3BwMN9//z27d++mYcOGt+zL1dWVlJSUAsetxEBERKQIODs755sI/JW7uzupqalWbbmPPTw8rNo7duxIXFwcY8eOJSEhgRYtWtCpUyfL9W5ubqSlpVk9Jy0tLU8/f0eJgYiISA5bJw3aQ61atbh69SoxMTH4+fkBcPr0aSpXroyXl5fVtVeuXKFZs2b069fP0ta7d2/atm1r6evkyZO0bNkSgMzMTM6dO2dVXvhfNMdAREQklx3nGBRUjRo1CAkJYdq0aSQlJXHhwgXmz59Pz54981y7Z88e+vXrx6VLl0hPT2fx4sWcPXuWxx57DIAePXqwbNkyjh07Rnp6Ou+99x5+fn40aNCgwPFoxEBERCRXCe18OHv2bCZNmkTr1q1xcHCgW7duDBkyBICgoCAmTpxIly5d6NChA2fOnKFPnz6kpKRw3333sWTJEnx9fQHo2bMniYmJvPDCC8TFxfHAAw8QFhaGk5NTgWMxmG1Z3FgExi3Ju3RD/r10EyW5WUPdREn+otLUxUXaf8rONXbry71ZL7v1VZw0YiAiIpLLxh0LyyIlBiIiIjlKYvLh7UapkYiIiFhoxEBERCSXHTc4Kq2UGIiIiOQwKzFQKUFERERu0IiBiIhILk0+VGIgIiKSS6UEJQYiIiI3aMRAcwxERETkBo0YiIiI5FIpQYmBiIhILu18qFKCiIiI3EQjBiIiIrlUSihcYvDtt9+yatUqLl26RMWKFenZsyedO3e2d2wiIiLFyoxKCQVOjRITEwHYsGEDo0aNonbt2vTr14+6desyYcIE1qyx3z2sRUREpGQUeMSgZ8+ezJ8/n4ULFzJ37lwaN25sOdeiRQsmTZpEr169iiRIERGR4qANjmxIDB599FHGjh1LVFQUoaGhVucaNWrEn3/+affgREREipUSg4KXEoYPH87ixYupXLkye/bssTq3Z88eqlatavfgREREpHjZNPnQ2dmZp556ihdeeIE+ffpQrVo1fv/9d1atWsUbb7xRVDGKiIgUC+1jUIhVCb169cJoNBIeHs63335LQEAAU6ZMoX379kURn4iISLHRHINCLlfs3r073bt3t3csIiIiJUsjBgVPDCZMmMCECRP+tmTw1ltv2SUoERERKRkFTgzMZnNRxiEiIlLiVEqwITGYOHEicH1U4Nq1a7i4uODi4sLp06epUKEC5cuXL7IgRUREioN2PizETZR+/vlnWrRowW+//QZc3wmxXbt2HDx40O7BiYiISPGyefLhjBkzGD16NPXr1weu729QrVo1pk2bxsqVK+0dn4iISLFRKaEQIwbnzp3Ls/Vx9+7dOXXqlN2CEhERKREGg/2OUsrmxMDX1zdP2eDw4cP4+fnZLSgREREpGTaXEp588kmee+45+vTpQ0BAAFFRUaxevZqhQ4cWRXwiIiLFxmz738tljs2JwVNPPYWXlxdffPEFW7ZsoUqVKowePZpOnToVRXwiIiLFRlsiF3LnwwcffJC2bdvi6enJgQMHKFeunL3jEhERkRJg85jJN998Q7du3Th37hwAv/zyC7169WLHjh32jk1ERKRYmQ0OdjtKK5tHDObOncv8+fO5//77Aejfvz81a9ZkxowZtGjRwu4BioiIFBdtcFSIEYM//viDZs2aWbU1bdqUqKgouwUlIiJSEjRiUIjEICAggJ07d1q17d69m6pVq9otKBERESkZNpcSnnvuOV544QXatm1rWa64detW3n777aKIT0REpNhoVUIhEoPOnTtTqVIlvvjiC44cOUKVKlX4+OOPCQ4OLor4REREio3mGBRyuWJoaCihoaH2jkVERERKmM2JQb9+/TDcYqjl008//ccBiYiIlJTSPGnQXmxODP46UhAfH8+mTZvo06eP3YISEREpCSolFCIxyO+eCN27d+edd96xS0AiIiJScgo1x+Cv7rvvPg4fPmyPrsoMD1fo0sSRGpUNmExw8IyJzXuzMZmtr+vX2pE7/a0zVBcnA3uOZ7Ph52zG9HWyOmcwgLOjgTU/ZHHorKmo34aUEGe/8jy0cxUHB71J3A+RJR2O2JnBw4ty3frjdNc9YMom7ZfdJG1aCaa8/6Zdg5ri3qIjDuV8yI6+RNLm1WSeO5HTkQG/sR9gMBgwm2/8col560XIzCiut1OmqJRQiMTgrxsZZWZm8vXXX1OlShW7BVUW9G7uyLUUM++uzsLTDfq2cqRJXQd2HbH+h790W5bV46CaDrR80Mh3v2YDMHVFptX57k2NeLgaOHJOSUFZVf6hYB5cNB2PmtVLOhQpIt6PD8F0LZ6Yt4fj4OmNT7+XcH+oHSk/fmN1nfM99fHq+hQJn80l48RBXO4NxuepEcTNG092zJ8YK1bFYDRyZdJgyM4uoXdTtqiUUIjEoFWrVlaTD81mM97e3kyZMsWugZVmFbzgrioOzFidQWY2xCfBjoMm2oYY8yQGN/MtBx1DjSzdmkVSat7z9QMdCKziwNz1mXlGHqRsCOjXjdrjX+TYGzMIXjGrpMORImCsUAnnu+8lZvpwyMzAFH+F5O/W49mud57EwPXBJqQd3E3G8V8BSD+6j4xzx3ENaUby5jU43XEXWX9eUFIgdmVzYrB+/Xo8PDwsj41GI76+vjg5Of3Ns/5dKvkYSEkzk3jTl/vlq2Z8PA24OkFaZv7P69zYkV9Omzh/Oe+3vosTtG9g5KuIbFLTiyhwKXExW34kasUGzNnZoMSgTDL6B2BKScKUeNXSlnU5CmN5Pwyu7pjTUm5cbHDAnPGXkoDZjGPF6yO0jnfchcHJmfLPj8NY3o+sy3+QtGUNWb+fKoZ3UjaplFCILZEHDx6Mt7c3AQEBBAQEULlyZSUFf+HsZCDDukJAZrY551z+z7mzkoE7/Ax8/0v+mX/jex24mmzmsEoIZVp6dMz1pEDKLIOzK+aMv2T3mek551ysmtOP7MU16CGcatQBBwec7w3C+e66GBydc56XSeaFMyQsn03MOyNIP3YAn6dH4FDerzjeSplkxmC3o7Qq1OTD1NRUPD097R1LmZGZZcbpL5+sk/H6D0n6LUYLGtR24PB5E0lp+Z8PqWVk+y2SBhEpPcyZ6RicnK0bna4nBOZ0618A6YcicPDwwuux/ji4uZN+/CBpB3+2PD/pm5VW16f+uAm34Ga41HmQ1J+3Fd2bKMO0JXIh9zHo1asXzZs3p1KlSlbn8lvK+G8UHW/Gw9WAhysk5/w7r+RjICHZnG9i4GCAe6o58Nl3WXlPAgF+1/vShEOR0i87+hIOHl4YPMphTr4GgGOlqmRfjcWcbj25yMHTm4yTh0j9+VtLW/lBY0k/shcAjzY9SD+8h6w/fr/xJEdHzJm3+AtEpABsTgwuXrxItWrVOHv2LGfPnrW032o3xH+juEQ4H23i0UZG1v+UjbsrtKjnwP6T+X+x+5c34OQIF/KZWwBQvZKBqFgzmRowECn1smOjyTh3HK+OfUn8YjEGD088WnYhbd/OPNc63VUHzw5PEB82BVPSNdwatcRYsTKpB34EwNE/AKfqtbm2cj6m1GTcm3fAwcWN9KP7ivttlRlms77LbE4M3n//fSpWrJin/eTJk3YJqKxY+X0WHUMdebmHE2Yz/HrGxPcHr3+zj+nrxIbd2RzM2YegvBekpkPWLQYEynsZuJaiZQgiZUXCinl4de6H76szwGwm7cAukr/7EgC/cR+S+OUS0n/dTfqhSIwVq1B+0JsYnF3JijrP1UVvY05OBODa2kV4Pvo4FYZNAicXsi6eIf7jdzCnJpfk2yvVzLZPvStzDOabd8UogODgYPbv32/Vlp2dTcOGDfO0F8S4JdqEQ24IHfhASYcgt5GGrzUp6RDkNlNp6uIi7f/k6fN266tWYOnci6RAIwbnz59nwIABmM1mUlNTad26tdX5tLQ0AgICiiRAERGR4lKaVxPYS4ESg+rVqzNmzBji4+OZMGFCnkmGLi4uNGzYsEgCFBERKS5KDGyYY9CyZUsA7rjjDho1alRkAYmIiEjJsXmWxV133cW0adMA2Lt3Lw899BAdO3bk1CnttCUiIqVbSW1wFBsby5AhQ2jQoAGhoaFMnTqVrKz8l7AvWbKEVq1aERwcTOfOndm8ebPlnMlkIigoiPr16xMUFGQ5UlJS8u0rPzavSpg0aRIpKSmYzWamTZtGhw4dcHNzY/LkySxZssTW7kRERG4bJVVKGD58OP7+/uzcuZOYmBief/55Fi9ezMCBA62u27FjB2FhYSxbtoy7776bzZs3M3z4cLZu3codd9zBqVOnyMzMZP/+/Tg7O9/i1f6ezYnBoUOH2LhxI1euXOG3335j0aJFeHl5ERoaWqgARERE/s3Onz9PZGQkP/zwA25ublSrVo0hQ4YwY8aMPInBmTNnMJvNlsNoNOLk5ISj4/Wv80OHDlGnTp1CJwVQiMQgNTUVV1dXtm7dSu3atSlfvjxJSUmWoEREREore25wlJGRQcZfboLl7Oyc50v75MmT+Pj44O/vb2kLDAwkKiqKa9euUa5cOUt7x44dCQ8Pp0OHDhiNRgwGAzNmzKBy5crA9cQgPT2dHj16cOnSJQIDAxkxYgTBwcEFjtvmOQb16tVjwoQJLFiwgDZt2hATE8Po0aM1IVFEREo9e84xCAsLIyQkxOoICwvL85rJycm4ublZteU+/uvcgMzMTO655x7WrFnDL7/8wqRJkxgzZgzHjx8HwNXVlXr16jF//ny+//57WrVqxYABA7hw4UKBPwObE4OpU6eSkZFBgwYNGDRoEJcuXSIjI4Px48fb2pWIiMhtxZ6JwaBBg9i3b5/VMWjQoDyv6e7uTmqq9X0ych97eHhYtU+ePJlatWpRr149nJ2d6dGjB/Xr12fdunUAjBo1imnTpuHv74+rqysDBgygatWq7Nixo8Cfgc3j/5UqVWL69OmWxw8++CAffvihrd2IiIiUafmVDfJTq1Ytrl69SkxMDH5+12+Zffr0aSpXroyXl5fVtVFRUdx///1WbY6Ojjg5OQEwc+ZM2rVrR926dS3nMzIycHGxvqX337E5MUhOTmbFihWcO3cOk8l6c/+33nrL1u5ERERuGyWxKqFGjRqEhIQwbdo0Jk2aRHx8PPPnz6dnz555rm3VqhXLli2jZcuW3HvvvWzZsoWIiAheeeUVAE6cOMHevXuZNWsW3t7eLFiwgKSkJNq0aVPgeGwuJbzxxht8+umnpKen2/pUERGR25rZbLDbYYvZs2eTlZVF69at6d27N82aNWPIkCEABAUFsX79egCGDh3Kk08+ybBhw2jYsCELFixg3rx53HvvvcD1P9DvvPNOunbtSmhoKJGRkXzyySf4+PgUOBabb6IUGhrK559/TrVq1Wx52i3pJkpyM91ESW6mmyjJXxX1TZQOnrxst77q1apkt76Kk82lBBcXF6slFSIiImWFSfdKsL2U0LdvX6ZPn05cXFxRxCMiIlJiSmpL5NuJzSMGq1evJioqis8++yzPud9++80uQYmIiEjJsDkxuHmpooiISFliz50PSyubE4NGjRphMpk4fPgwFy9epFKlSgQHB+PgYHNVQkRE5LZSmksA9mJzYnDlyhUGDx7MsWPH8PHxIT4+nho1avDxxx9b9moWERGR0snmP/PffvttatSoQWRkJLt27SIiIoJ7771XmxuJiEipV1L7GNxObB4x+Pnnn9m0aZNl/2YvLy8mTJhA69at7R6ciIhIcVIpoRCJgclkwmCw/uAMBoNln2YREZHSqjT/pW8vNpcSQkNDmTBhguVWkMnJyUyYMEG3XRYRESkDbB4xGDlyJP3796dRo0b4+Phw9epVAgMDWbBgQVHEJyIiUmxM//uSMs+mxMBsNpOVlcXXX3/N3r17iY2N5dKlSwwYMACj0VhUMYqIiBQLlRJsKCWkpKTwxBNP8M477+Do6Ejjxo1p3Lgxc+fOpV+/fpbSgoiIiJReBU4MPvjgA5ycnJg4caKlzdfXl++++46srCzCwsKKJEAREZHionsl2JAYbN68mSlTpuDr62vV7uvry8SJE9m0aZPdgxMRESlO2sfAhsQgNjaW6tWr53vu3nvv5cqVK3YLSkREREpGgRMDT09P4uPj8z139epV3Nzc7BaUiIhISVApwYbEoEmTJixfvjzfcytWrKB+/fr2iklERKREmMz2O0qrAi9XHDRoEN27dyc+Pp4OHTpQsWJFLl++zDfffMPatWtZtmxZUcYpIiIixaDAicFdd93FokWLGD9+PMuXL8dgMGA2m6lduzYLFy7k/vvvL8o4RUREilxpLgHYi00bHAUHB7NhwwYuXLhAXFwcFStWpGrVqkUVm4iISLEqzasJ7MXmLZEBqlWrRrVq1ewdi4iISIkyl+K5AfZi802UREREpOwq1IiBiIhIWWTSHAMlBiIiIrk0x0ClBBEREbmJRgxERERyaPKhEgMREREL7WOgUoKIiIjcRCMGIiIiOUrzPQ7sRYmBiIhIDq1KUClBREREbqIRAxERkRxalaDEQERExEI7HyoxEBERsdCIgeYYiIiIyE00YiAiIpJDqxKUGIiIiFhoHwOVEkREROQmGjEQERHJocmHSgxEREQsdBMllRJERETkJhoxEBERyaHJh0oMRERELDTH4DZIDLZ/vrukQ5DbyNDXmpR0CHIb2fOOfj+ItY5TSzqCsq/EEwMREZHbhUYMlBiIiIhYmLTzoRIDERGRXBox0HJFERERuYlGDERERHJoxECJgYiIiIX2MVApQURERG6iEQMREZEcZq1KUGIgIiKSS3MMVEoQERGRmygxEBERyWEy2++wRWxsLEOGDKFBgwaEhoYydepUsrKy8r12yZIltGrViuDgYDp37szmzZutzi9cuJDmzZtTv359+vXrx5kzZ2yKRYmBiIhIDrPZfocthg8fjru7Ozt37uTzzz9n9+7dLF68OM91O3bsICwsjI8++oj9+/czdOhQhg8fzsWLFwFYt24dS5cuZdGiRURERHDffffx4osvYrYhICUGIiIiJej8+fNERkYycuRI3NzcqFatGkOGDGH58uV5rj1z5gxms9lyGI1GnJyccHS8PmVw9erV9O3bl1q1auHi4sKIESOIiooiIiKiwPFo8qGIiEgOe04+zMjIICMjw6rN2dkZZ2dnq7aTJ0/i4+ODv7+/pS0wMJCoqCiuXbtGuXLlLO0dO3YkPDycDh06YDQaMRgMzJgxg8qVKwNw6tQpnn32Wcv1Tk5O1KhRg2PHjtG4ceMCxa0RAxERkRz2nGMQFhZGSEiI1REWFpbnNZOTk3Fzc7Nqy32ckpJi1Z6Zmck999zDmjVr+OWXX5g0aRJjxozh+PHjt+zL1dU1Tz9/RyMGIiIiOew5YjBo0CD69+9v1fbX0QIAd3d3UlNTrdpyH3t4eFi1T548meDgYOrVqwdAjx49+Oqrr1i3bh2jRo3Czc2NtLQ0q+ekpaXl6efvaMRARESkCDg7O+Pp6Wl15JcY1KpVi6tXrxITE2NpO336NJUrV8bLy8vq2qioqDzlCUdHR5ycnCx9nTx50nIuMzOTc+fOUbt27QLHrcRAREQkh8lkv6OgatSoQUhICNOmTSMpKYkLFy4wf/58evbsmefaVq1asWzZMo4cOYLJZGLTpk1ERETQoUMH4PoIwrJlyzh27Bjp6em89957+Pn50aBBgwLHo1KCiIhIjpLa+XD27NlMmjSJ1q1b4+DgQLdu3RgyZAgAQUFBTJw4kS5dujB06FCMRiPDhg0jISGB6tWrM2/ePO69914AevbsSWJiIi+88AJxcXE88MADhIWFWUYUCsJgtmVxYxFo2nlHSb683GbC631S0iHIbWTPO7tLOgS5zXTMPF6k/X+4+X9fU1CD29mvr+KkEQMREZEculeCEgMRERELW7cyLos0+VBEREQsNGIgIiKSw77T7gx27Kv4KDEQERHJoTkGKiWIiIjITTRiICIiksOWjYnKKiUGIiIiOVRKUGIgIiJioeWKmmMgIiIiN9GIgYiISA6VEpQYiIiIWJjtWksonfsYqJQgIiIiFhoxEBERyaHJh0oMRERELDTHQKUEERERuYlGDERERHKYVEtQYiAiIpJLpQSVEkREROQmGjEQERHJoREDJQYiIiIWJmUGtpcSkpOTWbx4MQCnTp2iV69ePPfcc0RHR9s7NhERkWJlNtnvKK1sTgwmT57MunXrAJgwYQJVq1bFx8eHCRMm2Ds2ERERKWY2lxIiIyMJDw8nISGB/fv389133+Hj40PTpk2LIj4REZFiY1YpwfbEIDk5GR8fHzZt2kS1atXw9/cnIyMDg6F03ixCREQkl6kUlwDsxebEoFatWsyfP58ffviBli1bkpSUxKxZs7jvvvuKIj4REREpRjbPMZgwYQK7d+/G09OToUOHcvToUSIiIhg3blxRxCciIlJszGaz3Y7SyuYRg5o1a7J06VLL40aNGrFhwwa7BiUiIlIStCOyDYnBggULeO6555g7d+4trxk6dKhdghIREZGSUeDEYM+ePTz33HNERETke16TD0VEpLQza8ig4InBwoULAazKCCIiImVJKZ4aYDeFuonS6dOnmTJlCkOHDiU+Pp5ly5bZOy4REREpATYnBrt27aJXr17Ex8fz008/kZaWxrx581iwYEFRxCciIlJsTCaz3Y7SyubE4P3332fmzJm89957GI1GqlSpwoIFC1i1alVRxCciIlJstFyxEMsVz58/T/PmzYEbEw4feOABEhIS7BuZiIhIMSvNNz+yF5sTg6pVq7J//35CQkIsbYcOHaJKlSp2Day08/F24rWhtQm634dsk5kt30Uz7+PTZP/lh+7dCQ9Qr663VZu7m5EvN0UxY95JnJ0MDH7qbv7zcEXc3Yz8fjGFD5ac5cChq8X3ZuQfM3h4Ua5bf5zuugdM2aT9spukTSvz3X/VNagp7i064lDOh+zoSyRtXk3muRM5HRnwG/sBBoPB6i+SmLdehMyM4no7Usyc/crz0M5VHBz0JnE/RJZ0OFLG2ZwYDBo0iOeff54nnniCzMxMFi5cyNKlS3nllVeKIr5Sa9JrdbkSm063p3fj6+PM9LH307vrHXy27qLVda9OOGT1uOMjlXmmb3U+XnEegMFP3c0DdcsxeOQBYuLS6fhIZd4Zdz//HbKH6CvpxfZ+5J/xfnwIpmvxxLw9HAdPb3z6vYT7Q+1I+fEbq+uc76mPV9enSPhsLhknDuJybzA+T40gbt54smP+xFixKgajkSuTBkN2dgm9GylO5R8K5sFF0/GoWb2kQ/lXMJXiEoC92DzHoGPHjsyYMYPffvuNqlWr8vPPPzNmzBi6detWBOGVTgFVXAmu58P8xWdITzcRFZ3G4pXn6dEp4G+fVy3AjZcH12Tiu8eIjb/+15+LiwOLlp/nckw6JhNs2PInmZkm6gR6FsdbETswVqiE8933krRpNWRmYIq/QvJ363Fr3DrPta4PNiHt4G4yjv8KZjPpR/eRce44riHNAHC64y6y/rygpOBfIqBfN+p/+i7Hx80s6VD+NTTHoBAjBocPH6ZFixa0aNGiKOIpE+6604OEa5nExt0Y2j13IYXKlVzx9DCSlJz/L/URz9fim23RHDx6Y77GjHknra4JrueDh7sjJ88mF03wYndG/wBMKUmYEq9a2rIuR2Es74fB1R1zWsqNiw0OmDP+UhIwm3GseL1U53jHXRicnCn//DiM5f3IuvwHSVvWkPX7qWJ4J1LcYrb8SNSKDZizs2HFrJIOR/4lbB4xePLJJ+natSvLli0jMTGxKGIq9dzdjKSlW3/55z52czXm+5x6dctxX51yfPLZuVv2e18dLya/XpePPzvHH9FpdotXipbB2RVzxl/KPpnpOedcrJrTj+zFNeghnGrUAQcHnO8NwvnuuhgcnXOel0nmhTMkLJ9NzDsjSD92AJ+nR+BQ3q843ooUs/TomOtJgRQbLVcsxIjBjz/+yIYNG/jiiy+YMWMGjzzyCD179qRJkyZFEV+plJaWjYuLdQLgmvM4JTX/f+Rd2ldl+49XiLuame/5Tm0r8+LAmixafo5VX17M9xq5PZkz0zE4OVs3Ol1PCMzp1gle+qEIHDy88HqsPw5u7qQfP0jawZ8tz0/6ZqXV9ak/bsItuBkudR4k9edtRfcmRP4lSnEFwG5sTgy8vLzo27cvffv25fTp06xfv5433ngDJycntm7dWhQxljpnzqfgU86J8j5OxOd80deo5k70lTSSU/ImBkYHaBbqyxtTj+Q55+BwvcTQvIkfo6ceZu+vV4s6fLGz7OhLOHh4YfAohzn5GgCOlaqSfTUWc3qq1bUOnt5knDxE6s/fWtrKDxpL+pG9AHi06UH64T1k/fH7jSc5OmLOzD+hFBGxVaG2RAZISUnh4MGDHDp0iISEBOrVq2fPuEq1i3+k8uuRBF4aWBM3NyNV/F15+vHqfL31z3yvD7zLExdnBw7/lncviGEDA2kcUoGBL+9XUlBKZcdGk3HuOF4d+2JwdsWhvB8eLbuQtm9nnmud7qqDz8BROPj4gqMTbg+1xVixMqkHfgTA0T8Az45P4uDpDUZH3Ft2wcHFjfSj+4r7bYmUSWaT2W5HaWXziMFPP/3EunXr+Pbbb7njjjvo2bMnM2fOxNvb+38/+V/kzelHeGVQLdZ8FIrZZGbTd9EsXnV9CeKW1U2ZMe8EW3dcBqCqvyvXErPIyLT+QfIu50j3DgGYTGaWzmtode7m58vtL2HFPLw698P31RlgNpN2YBfJ330JgN+4D0n8cgnpv+4m/VAkxopVKD/oTQzOrmRFnefqorcxJ1+fz3Nt7SI8H32cCsMmgZMLWRfPEP/xO5hTNRlVxB60XBEMZhvXVAQFBdGxY0d69uxJ/fr1/3EATTvv+Md9SNkRXu+Tkg5BbiN73tld0iHIbaZj5vEi7X/YrGt262vO8HJ266s42Txi0KFDB0aNGoWnp9bRi4hI2VKaSwD2YvMcg2+//RY3N7eiiEVERKREaY5BIUYMunfvzsSJE+nevTsVK1a03EgJrt9HQUREpLQqxd/ndmNzYvDJJ9drwKtXrwaw3MzFYDDw22+/2Tc6ERERKVY2JwbbtmkTFRERKZtKcwnAXmyeYxAQEEBAQAAJCQkcOXKEihUr4urqSkDA398gSERE5HanmygVIjGIjY3l8ccfp3fv3rz++utcuHCBRx55hAMHDhRFfCIiIlKMbE4Mpk2bRu3atdmzZw+Ojo4EBgby3HPP8c477xRFfCIiIsVGN1EqxByDn3/+2bJkMXdFwsCBA/n444/tHpyIiEhxKs0lAHuxecTAycmJtLTrd4TL/QCTk5Px8PCwb2QiIiJS7GxODFq1asXIkSM5d+4cBoOB2NhYJk6cSIsWLYoiPhERkWKjDY4KUUoYMWIEb7zxBu3btwegadOmtGjRgkmTJtk9OBERkeJUUl/osbGxjB07lsjISIxGI126dOH111/H0dH6a3rgwIHs22d9N9WUlBT69OnDpEmTMJlMhISEWPYXyrVr1y7c3d0LFItNiYHJZCIjI4PZs2cTFxfH2rVryczMpH379nh5ednSlYiIiOQYPnw4/v7+7Ny5k5iYGJ5//nkWL17MwIEDra776KOPrB5//vnnzJ07l6FDhwJw6tQpMjMz2b9/P87OzoWKpcClhOjoaDp37mxZfbBr1y5mzpzJt99+S+/evTl06FChAhAREbldmMxmux0ZGRkkJSVZHRkZGXle8/z580RGRjJy5Ejc3NyoVq0aQ4YMYfny5X8b65kzZ5g8eTLvvvsulSpVAuDQoUPUqVOn0EkB2JAYzJw5kzp16vDqq68CMGfOHJ599lnCw8MZN24cc+bMKXQQIiIitwN7zjEICwsjJCTE6ggLC8vzmidPnsTHxwd/f39LW2BgIFFRUVy7duvbQE+cOJFu3brRoEEDS9uhQ4dIT0+nR48eNG7cmCeffJL9+/fb9BkUuJSwa9cuvvzySypUqEBUVBS///47Xbp0AaB169ZMmTLFphcWERG53dhzueKgQYPo37+/VVt+f8knJyfnuWtx7uOUlBTKlSuX5zl79+7l119/5d1337Vqd3V1pV69erz00kt4e3uzfPlyBgwYwPr166lWrVqB4i5wYpCUlESFChUA+PXXXylXrhyBgYEAuLi4kJmZWdCuREREyjxnZ+cCDem7u7uTmppq1Zb7+FZbAaxatYpHH32UihUrWrWPGjXK6vGAAQMIDw9nx44d/Pe//y1Q3AUuJXh7exMXFwdAZGQkwcHBlnNnzpyhfPnyBe1KRETktlQSOx/WqlWLq1evEhMTY2k7ffo0lStXzndif1ZWFtu2bbOM2t9s5syZHD161KotIyMDFxeXAsdT4MSgZcuWTJ48mY0bN7JhwwY6duwIwLVr1/i///s/mjVrVuAXFRERuR2VxD4GNWrUICQkhGnTppGUlMSFCxeYP38+PXv2zPf648ePk56ebvUHeq4TJ04wdepUrly5QkZGBnPnziUpKYk2bdoUOJ4CJwYvv/wyCQkJjB49mnbt2tG5c2cAWrRowcmTJxk2bFiBX1RERERumD17NllZWbRu3ZrevXvTrFkzhgwZAkBQUBDr16+3XHvhwgW8vb3zHQV46623uPPOO+natSuhoaFERkbyySef4OPjU+BYDOZ/ONPixx9/pGHDhjYNU9ysaecd/+TlpYwJr/dJSYcgt5E97+wu6RDkNtMx83iR9t931EW79bVi+h1266s42bzz4V81bdrUHnGIiIiUOLPJVNIhlDib75UgIiIiZdc/HjEQEREpK2xZTVBWKTEQERHJYc8NjkorlRJERETEQiMGIiIiOUrqtsu3EyUGIiIiOZQYKDEQERGxMJm1XFFzDERERMRCIwYiIiI5VEpQYiAiImKhxEClBBEREbmJRgxERERyaIMjJQYiIiIWJt1ESaUEERERuUEjBiIiIjk0+VCJgYiIiIVZGxyplCAiIiI3aMRAREQkh0oJSgxEREQslBgoMRAREbHQTZQ0x0BERERuohEDERGRHColKDEQERGxMGvnQ5USRERE5AaNGIiIiORQKUGJgYiIiIV2PlQpQURERG6iEQMREZEcJpUSlBiIiIjk0qoElRJERETkJhoxEBERyaFVCUoMRERELLQqQYmBiIiIhUYMNMdAREREbqIRAxERkRxalQAGs9mscRMREREBVEoQERGRmygxEBEREQslBiIiImKhxEBEREQslBiIiIiIhRIDERERsVBiICIiIhZKDERERMRCiYGIiIhYKDEoBRITE4mLiyvpMKSInTt3rqRDEBFRYvC/9OvXjzlz5hTquXXq1CEiIsKqLSEhgZCQED777LMC99OmTRtOnjx5y/MDBw7kww8/LFSMYu3s2bO8/vrrNG/enKCgIB555BHeffddkpOTi/R1t2/fzoABA4r0NfIzbtw4xo0bl6d91qxZtG3bloSEhGKPqbRLSEhgwoQJtGjRgvr169O0aVNef/11/vzzz2KPRb8bpDB0E6VitmXLFrp3784TTzxR4OfEx8f/7fmPPvron4YlwP79+3nmmWd45pln+OKLL6hQoQJnz55l3LhxPPPMM6xYsQKj0Vgkr3316lVK4rYlkyZNytOWkZFBREQE8+bNw9vbu9hjKu1efvllvLy8+Pzzz6lYsSIxMTFMnTqV/v37s2HDBhwdi+/Xrn43SGFoxKCAwsPDeeKJJ5gyZQqNGzemSZMmjBkzhszMTAAyMzN56623CA0NpXHjxnn+QbZq1Yrw8HB69erFmDFjiIiIoE6dOpbzc+bMoUWLFjRq1IgePXqwbds2ANq1awfAs88+y8KFCwkPD6d79+4888wzNGjQgA0bNliNaiQlJfHmm2/Stm1b6tevT7NmzfQXQwGNGzeObt268eKLL1KhQgUA7rrrLmbOnImvry8XLlzg0qVLDB8+nCZNmvDwww8zYsQILl++DEBERAStWrXigw8+oFmzZjRq1Ihhw4aRlJQEQHR0NAMHDqRRo0Y0b96coUOHcvnyZSIiIhg/fjxRUVEEBQURHR1Nv379GDVqFC1btuQ///kPx48fp06dOly8eNES75w5c+jXr5/l8YYNG+jUqRNBQUE8+uijbNy40XJuyZIltGnThqCgILp3787u3bsBGDVqFKNGjbJct2bNGh577DGOHz/OK6+8wvr164vuAy+j9u3bR5s2bahYsSIAfn5+jB49mgcffJBr166RlJTEpEmTaNGiBU2aNOHll18mJiYGgIsXL1KnTh2mT59Ow4YNGT16NEFBQfz444+W/q9du0a9evU4ePAgAF9//TWdO3cmJCSE7t27W1178++GkydP8uSTT9KwYUNatmzJ66+/bvnZFLmZEgMb7N+/H19fX3bu3ElYWBgbN25ky5YtAMyfP5/vv/+ezz//nO3bt3PixIkC9/vzzz+zatUq1qxZQ0REhCV5yMzMZPPmzQAsXLiQZ599FoAjR47QuXNnfvrpJ9q0aWPV17vvvsvFixf5/PPPOXDgAG+++SYzZ87k/PnzdvoUyqbff/+dkydP0qlTpzzn/Pz8mD9/PgEBATzzzDMYjUa2bNnCN998A8DgwYPJysoC4NKlS0RHR7N161bWrFnDgQMHWLFiBQDvv/8+lStXZteuXWzcuJGUlBQWLFhAaGgoEydOpGrVqhw4cAB/f38AfvrpJ1auXMn69evx8PD42/gjIiIYPXo0I0eOZN++fbzxxhu89tprnDp1ivDwcObPn88777zDvn37eOKJJ3j++ee5evWqVR/h4eFMnz6dN998kz179jB69GgmTpzI1q1b/+nH+6/SsWNHxo8fz4QJE9i4cSOXLl2iYsWKTJ8+nQoVKjB69GjOnz9PeHg43377LZ6engwdOtRqxCg5OZldu3YxatQoHn30UdatW2c599VXX1G9enXq1avHjh07GD9+POPGjSMyMpJhw4YxbNiwfEuPEydOpEmTJkRGRrJ27VqOHj3KmjVriuUzkdJFpQQbuLq6MnjwYAwGA/Xq1aNOnTqcPXsWgC+//JLBgwdTrVo1AN58880C/7Xl4uJCQkICq1evpmXLlvTq1Ys+ffpgMBjyvd7JyYmuXbvi4JA3rxs2bBhGoxFPT0/+/PNPXFxcALh8+TLVq1cvzNv+V8id3Onn53fLa/bu3cuFCxdYu3Ytnp6ewPVfto0aNeLw4cOW61544QVcXV2pXr06oaGhlp8RFxcX9uzZw9dff02TJk346KOP8v1/mKt58+aWJOHatWt/G/8XX3xB27ZtadGiheW5K1aswN/fn3Xr1tGnTx+CgoIA6NWrF4GBgbi6ulr1sXbtWvr06UOTJk0AaNKkCX369GHlypV5ElC5tSlTphAaGsrGjRsZN24ciYmJ3HnnnQwbNoyHH36YzZs388033+Dr6wvA6NGjadCgAUeOHMHHxweAbt264ezsjLOzM7169aJ///4kJSXh6enJunXr6NmzJwDLli3jiSeeoGHDhgC0bNmSVq1asXLlSsaOHWsVl4uLCzt37iQwMJAmTZrw5Zdf/u3Pn/x76afCBr6+vlZf1k5OTpYs//Lly1SpUsVyrly5cgWuzwYFBTFnzhwOHDjAk08+ycMPP8z8+fMxmUz5Xl+xYsVb/oOOjY3lpZdeIjQ0lCFDhlhKErfqS67LHfa9cuVKvudjYmKIjY2lfPnylqQAwNPTEx8fHy5dupSnL7D+GXnzzTfp0KEDixYtokWLFnTv3p29e/feMqZKlSoVOP7Lly9TtWpVq7Z69erh5eXFlStX8pwLDg7OkxjExMRYEttcd9xxh9V7k//NwcGBrl27EhYWZkkE27dvz2uvvcbatWsB6N27Nw0aNKBBgwY0a9YMo9FoVSa6+f99UFAQd9xxB5s3b+b06dMcO3aMrl27AtdHqD799FNLXw0aNGD79u1ERUXliWvWrFk8+OCDzJw5kyZNmtCvX7+/ndQs/15KDOykcuXKXLhwwfI4JSWFxMREy2MHBwfLfASwnlAYFRWFr68vixYtIjIykrfffpsPP/yQH374Id/XutVIAsBLL73E/fffz+7du1m3bh2vvPLKP3lb/xoBAQHUrl3bqi6fKzY2lpYtW3Lp0iXi4+Ot6rKJiYnEx8dbJQO3cvToUfr06cOGDRv46aefCAkJYejQobe8/ub/z7mTHm/1M1SlSpU8XwYff/wx+/fvp0qVKvzxxx9W52bOnMnp06et2u644w5+//13q7YLFy4U6L3JdTt37iQoKMhSpjEYDNSsWZMRI0ZQt25dMjIyAPjmm2/Yu3ev5QgPD6dly5aWfv76b7xnz5589dVXfPnllzzyyCOWkYXKlSvzwgsvWPX19ddfM3XqVKvnm0wmjh49yrBhw9iyZQvbt2/H19fXan6JSC4lBnbSq1cvPvroI06fPk16ejrTp08nOzvbcj4wMJBt27aRlpbGlStX+PTTTy3nDh06xMCBAzl27BjOzs6WIcby5csD4OzsbJVk/J3ExERcXV0xGo3ExcUxZcoUwPoLRfI3duxY1q5dy9y5c4mPj8dsNvPbb78xePBg7rvvPp555hlq1qzJ+PHjSUxMJDExkQkTJnDnnXcSHBz8P/v/8MMPmTx5MklJSZQrVw43NzfL/2MXFxdSU1MtcxX+ytfXF29vb77++mvMZjNHjhxh06ZNlvOPPfYYW7du5ccff8RkMrFz507mzJmDl5cX3bt3Z9WqVRw8eBCTycTatWtZvny55bVz9ezZk1WrVrF7926ys7Mtc1969OjxDz7Vf5eGDRvi6+vLG2+8wfHjx8nMzCQpKYn169dz7tw5Hn30Uf7zn/8wdepU4uPjyczM5IMPPqBnz55/Wy7q1q0bv/zyC1988QW9evWytPfu3ZtPP/3UMhHx0KFDdO/ena+++srq+Q4ODkyZMoVZs2aRnp5OhQoVcHFxyfMzIAKaY2A3zz77LKmpqfz3v/8lKyuL3r17W7J6gFdffZUJEybw8MMPU6lSJZ566in27dsHXF95cO7cOZ5//nni4+Px9fW1zGIG6NOnDyNGjODpp5/+n/ME3nrrLaZNm8bHH3+Mt7c3HTp0oG7dupw4cYKmTZsW2fsvCxo1asSyZcv48MMP6dixI6mpqfj5+dG+fXsGDRqEk5MTYWFhTJ8+nXbt2pGRkcFDDz3EJ598UqAlaJMmTWLixIm0bt2ajIwM7r//fv7v//4PuPGF0rBhQ1auXJnnuc7OzkyePJnZs2ezaNEi7r//fnr37m35GQoJCeHtt9/m7bff5tKlSwQEBPD+++9Tq1YtatWqxbVr1xg5ciRXrlyhZs2aLFy40LLyItejjz5KUlISU6ZMISoqCn9/f1577TW6dev2zz/cfwlXV1dWrFjB3Llzef7554mNjcXJyYn69evzySefEBgYyDvvvMN7771Ht27dSEpKolatWnz00UdUrFjRqpxwMx8fH1q1asWBAwcsc0AA2rdvT0pKCqNHjyYqKgofHx+efvppq9UquWbNmsXkyZNp2rQpJpOJhg0bMnny5CL7LKT0MphLYvG0iIiI3JZUShARERELJQYiIiJiocRARERELJQYiIiIiIUSAxEREbFQYiAiIiIWSgxERETEQomBiIiIWCgxEBEREQslBiIiImKhxEBEREQs/h++FrFvrduGbgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calcula la matriz de correlación\n",
    "corr = datos[['Indústria', 'Construcció', 'Serveis']].corr()\n",
    "\n",
    "# Crea un mapa de calor de la matriz de correlación\n",
    "sns.heatmap(corr, annot=True, cmap='coolwarm', xticklabels=corr.columns, yticklabels=corr.columns)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "dd7d9915",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x750 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(datos[['Indústria', 'Construcció', 'Serveis']])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "8d0362a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Relación entre Industria y Construcción: y = 0.74x + -3011.51\n",
      "Relación entre Industria y Servicios: y = 7.13x + -25652.04\n",
      "Relación entre Construcción y Servicios: y = 9.89x + 2822.75\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Ajuste de un polinomio de primer grado (una línea) para Industria vs Construcción\n",
    "coef_ind_con = np.polyfit(datos['Indústria'], datos['Construcció'], 1)\n",
    "print(f\"Relación entre Industria y Construcción: y = {coef_ind_con[0]:.2f}x + {coef_ind_con[1]:.2f}\")\n",
    "\n",
    "# Ajuste de un polinomio de primer grado (una línea) para Industria vs Servicios\n",
    "coef_ind_ser = np.polyfit(datos['Indústria'], datos['Serveis'], 1)\n",
    "print(f\"Relación entre Industria y Servicios: y = {coef_ind_ser[0]:.2f}x + {coef_ind_ser[1]:.2f}\")\n",
    "\n",
    "# Ajuste de un polinomio de primer grado (una línea) para Construcción vs Servicios\n",
    "coef_con_ser = np.polyfit(datos['Construcció'], datos['Serveis'], 1)\n",
    "print(f\"Relación entre Construcción y Servicios: y = {coef_con_ser[0]:.2f}x + {coef_con_ser[1]:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a457d8fe",
   "metadata": {},
   "source": [
    "## Regresión Multiple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "c95313b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  Total   R-squared:                       1.000\n",
      "Model:                            OLS   Adj. R-squared:                  1.000\n",
      "Method:                 Least Squares   F-statistic:                 2.796e+06\n",
      "Date:                Wed, 04 Oct 2023   Prob (F-statistic):          2.51e-175\n",
      "Time:                        13:09:40   Log-Likelihood:                -244.09\n",
      "No. Observations:                  73   AIC:                             496.2\n",
      "Df Residuals:                      69   BIC:                             505.3\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===============================================================================\n",
      "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------\n",
      "const          50.6885     35.178      1.441      0.154     -19.491     120.867\n",
      "Indústria       0.9993      0.006    156.927      0.000       0.987       1.012\n",
      "Construcció     1.0881      0.015     73.217      0.000       1.058       1.118\n",
      "Serveis         0.9927      0.001    766.567      0.000       0.990       0.995\n",
      "==============================================================================\n",
      "Omnibus:                        3.746   Durbin-Watson:                   1.851\n",
      "Prob(Omnibus):                  0.154   Jarque-Bera (JB):                3.635\n",
      "Skew:                          -0.537   Prob(JB):                        0.162\n",
      "Kurtosis:                       2.797   Cond. No.                     1.09e+06\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.09e+06. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "# Definir tus variables independientes y dependientes\n",
    "X = datos[['Indústria', 'Construcció', 'Serveis']]\n",
    "y = datos['Total']\n",
    "\n",
    "# Agregar una constante a las variables independientes para el término de intercepción\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "# Ajustar el modelo\n",
    "modelo = sm.OLS(y, X).fit()\n",
    "\n",
    "# Imprimir los resultados\n",
    "print(modelo.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "b7722365",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coeficientes: [0.99933033 1.08805717 0.99265478]\n",
      "Intercepción: 50.68845717301883\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# Definir tus variables independientes y dependientes\n",
    "X = datos[['Indústria', 'Construcció', 'Serveis']]\n",
    "y = datos['Total']\n",
    "\n",
    "# Crear y entrenar el modelo\n",
    "modelo = LinearRegression().fit(X, y)\n",
    "\n",
    "# Imprimir los coeficientes\n",
    "print('Coeficientes:', modelo.coef_)\n",
    "print('Intercepción:', modelo.intercept_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edab868a",
   "metadata": {},
   "source": [
    "### Total=0.9993×(Indústria)+1.0881×(Construcción)+0.9927×(Serveis)+50.6885"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "6d632aae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Total    Predicción\n",
      "0   35970  35964.134633\n",
      "1   36260  36259.519742\n",
      "2   36425  36420.352688\n",
      "3   36660  36652.162148\n",
      "4   36675  36677.388774\n",
      "..    ...           ...\n",
      "68  28860  28858.284285\n",
      "69  29033  29041.287586\n",
      "70  29581  29573.431730\n",
      "71  29589  29586.289746\n",
      "72  29579  29582.566181\n",
      "\n",
      "[73 rows x 2 columns]\n",
      "        Fecha  Agricultura  Indústria  Construcció  Serveis  Total   Año  \\\n",
      "0  2023-08-01           70       7145         2405    26350  35970  2023   \n",
      "1  2023-07-01           70       7170         2430    26595  36260  2023   \n",
      "2  2023-06-01           70       7185         2450    26720  36425  2023   \n",
      "3  2023-05-01           65       7180         2435    26975  36660  2023   \n",
      "4  2023-04-01           65       7170         2440    27005  36675  2023   \n",
      "..        ...          ...        ...          ...      ...    ...   ...   \n",
      "68 2013-03-01           60       6631         1833    20336  28860  2013   \n",
      "69 2012-12-01           55       6676         1896    20406  29033  2012   \n",
      "70 2012-09-01           74       6843         1958    20706  29581  2012   \n",
      "71 2012-06-01           76       6948         2023    20542  29589  2012   \n",
      "72 2012-03-01           72       6968         2045    20494  29579  2012   \n",
      "\n",
      "     Media Móvil  Volatilidad    Predicción  \n",
      "0            NaN          NaN  35964.134633  \n",
      "1            NaN          NaN  36259.519742  \n",
      "2   36218.333333          NaN  36420.352688  \n",
      "3   36448.333333          NaN  36652.162148  \n",
      "4   36586.666667          NaN  36677.388774  \n",
      "..           ...          ...           ...  \n",
      "68  28847.666667   840.666024  28858.284285  \n",
      "69  28924.000000   750.955432  29041.287586  \n",
      "70  29158.000000   646.226600  29573.431730  \n",
      "71  29401.000000   506.327979  29586.289746  \n",
      "72  29583.000000   418.203409  29582.566181  \n",
      "\n",
      "[73 rows x 10 columns]\n"
     ]
    }
   ],
   "source": [
    "# Hacer una predicción\n",
    "predicciones = modelo.predict(X)\n",
    "\n",
    "# Agregar las predicciones al DataFrame\n",
    "datos['Predicción'] = predicciones\n",
    "\n",
    "# Ver los resultados\n",
    "print(datos[['Total', 'Predicción']])\n",
    "print(datos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "5d94ac5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error Cuadrático Medio: 46.96447134235665\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# Calcular el error cuadrático medio\n",
    "mse = mean_squared_error(datos['Total'], datos['Predicción'])\n",
    "\n",
    "# Imprimir el error cuadrático medio\n",
    "print('Error Cuadrático Medio:', mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "1bf54ac5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Indústria  Construcción  Servicios  Total Estimado\n",
      "0        7068          2016      20592           29748\n",
      "1        7108          2045      20537           29765\n",
      "2        7359          1903      20643           29966\n",
      "3        7168          2145      20611           30007\n",
      "4        6774          1941      21544           30317\n",
      "5        7186          1854      21267           30359\n",
      "6        7216          1955      21152           30385\n",
      "7        6518          1900      21970           30440\n",
      "8        7232          1900      21309           30497\n",
      "9        7306          1999      21488           30856\n",
      "10       6868          1800      22771           31476\n",
      "11       6905          1814      22754           31511\n",
      "12       6579          2107      22767           31517\n",
      "13       6957          2411      22395           31856\n",
      "14       7272          2231      22290           31871\n",
      "15       7354          2170      22323           31919\n",
      "16       7263          2322      22359           32030\n",
      "17       7264          1856      22946           32106\n",
      "18       7148          2220      22872           32313\n",
      "19       7206          2447      22828           32574\n",
      "20       7286          2337      23123           32827\n",
      "21       6995          2029      23791           32864\n",
      "22       7313          2260      23301           32947\n",
      "23       6701          2447      24098           33330\n",
      "24       6683          2063      24562           33355\n",
      "25       6786          1825      24800           33435\n",
      "26       7193          2315      24014           33595\n",
      "27       6624          2405      24641           33747\n",
      "28       7356          2096      24518           34020\n",
      "29       6823          2214      24987           34081\n",
      "30       6940          1819      25304           34083\n",
      "31       6608          1952      25536           34126\n",
      "32       6596          1829      26155           34595\n",
      "33       6656          2332      25630           34681\n",
      "34       7214          2315      25386           34978\n",
      "35       6797          2353      25827           35040\n",
      "36       6932          2077      26010           35056\n",
      "37       7218          1975      25867           35089\n",
      "38       6686          1915      26512           35133\n",
      "39       6981          2160      25968           35154\n",
      "40       7264          2432      25638           35405\n",
      "41       7138          1923      26337           35419\n",
      "42       6581          2383      26398           35424\n",
      "43       7344          2282      25748           35431\n",
      "44       7046          1863      26603           35526\n",
      "45       7060          2093      26503           35691\n",
      "46       7053          1867      26766           35699\n",
      "47       7109          1891      26857           35872\n",
      "48       7109          2389      26362           35922\n",
      "49       7358          2202      26578           36182\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Coeficientes e intercepción del modelo\n",
    "coeficientes = np.array([0.99933033, 1.08805717, 0.99265478])\n",
    "intercepcion = 50.68845717301883\n",
    "\n",
    "# Generar valores enteros aleatorios para Industria, Construcción y Servicios basados en los rangos existentes en los datos\n",
    "np.random.seed(0)  # Para reproducibilidad\n",
    "industria_aleatoria = np.random.randint(datos['Indústria'].min(), datos['Indústria'].max() + 1, 50)\n",
    "construccion_aleatoria = np.random.randint(datos['Construcció'].min(), datos['Construcció'].max() + 1, 50)\n",
    "servicios_aleatorios = np.random.randint(datos['Serveis'].min(), datos['Serveis'].max() + 1, 50)\n",
    "\n",
    "# Calcular el total basado en la ecuación del modelo\n",
    "total_predicho = coeficientes[0] * industria_aleatoria + coeficientes[1] * construccion_aleatoria + coeficientes[2] * servicios_aleatorios + intercepcion\n",
    "\n",
    "# Convertir el total predicho a entero\n",
    "total_predicho = total_predicho.astype(int)\n",
    "\n",
    "# Crear un DataFrame para mostrar los resultados\n",
    "resultados = pd.DataFrame({\n",
    "    'Indústria': industria_aleatoria,\n",
    "    'Construcción': construccion_aleatoria,\n",
    "    'Servicios': servicios_aleatorios,\n",
    "    'Total Estimado': total_predicho\n",
    "})\n",
    "\n",
    "# Ordenar los resultados de menor a mayor basado en el total estimado\n",
    "resultados_ordenados = resultados.sort_values(by='Total Estimado').reset_index(drop=True)\n",
    "\n",
    "print(resultados_ordenados)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "40584e49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Indústria  Coef*Indústria  Construcción  Coef*Construcción  Servicios  \\\n",
      "0        6692     6687.518568          1808        1967.207363      20543   \n",
      "1        6782     6777.458298          2047        2227.253027      20360   \n",
      "2        6701     6696.512541          1829        1990.056564      20973   \n",
      "3        7272     7267.130160          1891        2057.516108      20430   \n",
      "4        7218     7213.166322          2028        2206.579941      20361   \n",
      "..        ...             ...           ...                ...        ...   \n",
      "68       7264     7259.135517          2155        2344.763201      26051   \n",
      "69       7311     7306.104043          1815        1974.823764      26591   \n",
      "70       7019     7014.299586          2183        2375.228802      26521   \n",
      "71       7068     7063.266772          2029        2207.667998      26766   \n",
      "72       6981     6976.325034          2096        2280.567828      26930   \n",
      "\n",
      "    Coef*Servicios  Total Estimado  \n",
      "0     20392.107146           29097  \n",
      "1     20210.451321           29265  \n",
      "2     20818.948701           29556  \n",
      "3     20279.937155           29655  \n",
      "4     20211.443976           29681  \n",
      "..             ...             ...  \n",
      "68    25859.649674           35514  \n",
      "69    26395.683255           35727  \n",
      "70    26326.197420           35766  \n",
      "71    26569.397841           35891  \n",
      "72    26732.193225           36039  \n",
      "\n",
      "[73 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Coeficientes e intercepción del modelo\n",
    "coeficientes = np.array([0.99933033, 1.08805717, 0.99265478])\n",
    "intercepcion = 50.68845717301883\n",
    "\n",
    "# Generar valores enteros aleatorios para Industria, Construcción y Servicios basados en los rangos existentes en los datos\n",
    "np.random.seed(0)  # Para reproducibilidad\n",
    "industria_aleatoria = np.random.randint(datos['Indústria'].min(), datos['Indústria'].max() + 1, 73)\n",
    "construccion_aleatoria = np.random.randint(datos['Construcció'].min(), datos['Construcció'].max() + 1, 73)\n",
    "servicios_aleatorios = np.random.randint(datos['Serveis'].min(), datos['Serveis'].max() + 1, 73)\n",
    "\n",
    "# Calcular el total basado en la ecuación del modelo\n",
    "total_predicho = coeficientes[0] * industria_aleatoria + coeficientes[1] * construccion_aleatoria + coeficientes[2] * servicios_aleatorios + intercepcion\n",
    "\n",
    "# Convertir el total predicho a entero\n",
    "total_predicho = total_predicho.astype(int)\n",
    "\n",
    "# Calcular los valores de los coeficientes multiplicados por las variables\n",
    "coef_industria = coeficientes[0] * industria_aleatoria\n",
    "coef_construccion = coeficientes[1] * construccion_aleatoria\n",
    "coef_servicios = coeficientes[2] * servicios_aleatorios\n",
    "\n",
    "# Crear un DataFrame para mostrar los resultados\n",
    "resultados = pd.DataFrame({\n",
    "    'Indústria': industria_aleatoria,\n",
    "    'Coef*Indústria': coef_industria,\n",
    "    'Construcción': construccion_aleatoria,\n",
    "    'Coef*Construcción': coef_construccion,\n",
    "    'Servicios': servicios_aleatorios,\n",
    "    'Coef*Servicios': coef_servicios,\n",
    "    'Total Estimado': total_predicho\n",
    "})\n",
    "\n",
    "# Ordenar los resultados de menor a mayor basado en el total estimado\n",
    "resultados_ordenados = resultados.sort_values(by='Total Estimado').reset_index(drop=True)\n",
    "\n",
    "print(resultados_ordenados)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "a99b7efc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Industria_Modelo  Indústria Tendencia_Industria  Construccion_Modelo  \\\n",
      "0               7140       7145           Disminuye                 2616   \n",
      "1               7165       7170           Disminuye                 2643   \n",
      "2               7180       7185           Disminuye                 2665   \n",
      "3               7175       7180           Disminuye                 2649   \n",
      "4               7165       7170           Disminuye                 2654   \n",
      "..               ...        ...                 ...                  ...   \n",
      "68              6626       6631           Disminuye                 1994   \n",
      "69              6671       6676           Disminuye                 2062   \n",
      "70              6838       6843           Disminuye                 2130   \n",
      "71              6943       6948           Disminuye                 2201   \n",
      "72              6963       6968           Disminuye                 2225   \n",
      "\n",
      "    Construcció Tendencia_Construccion  Servicios_Modelo  Serveis  \\\n",
      "0          2405                Aumenta             26156    26350   \n",
      "1          2430                Aumenta             26399    26595   \n",
      "2          2450                Aumenta             26523    26720   \n",
      "3          2435                Aumenta             26776    26975   \n",
      "4          2440                Aumenta             26806    27005   \n",
      "..          ...                    ...               ...      ...   \n",
      "68         1833                Aumenta             20186    20336   \n",
      "69         1896                Aumenta             20256    20406   \n",
      "70         1958                Aumenta             20553    20706   \n",
      "71         2023                Aumenta             20391    20542   \n",
      "72         2045                Aumenta             20343    20494   \n",
      "\n",
      "   Tendencia_Servicios  Total_Modelo  Total Tendencia_Total  \n",
      "0            Disminuye         35962  35970       Disminuye  \n",
      "1            Disminuye         36257  36260       Disminuye  \n",
      "2            Disminuye         36418  36425       Disminuye  \n",
      "3            Disminuye         36650  36660       Disminuye  \n",
      "4            Disminuye         36675  36675       Disminuye  \n",
      "..                 ...           ...    ...             ...  \n",
      "68           Disminuye         28856  28860       Disminuye  \n",
      "69           Disminuye         29039  29033         Aumenta  \n",
      "70           Disminuye         29571  29581       Disminuye  \n",
      "71           Disminuye         29585  29589       Disminuye  \n",
      "72           Disminuye         29581  29579         Aumenta  \n",
      "\n",
      "[73 rows x 12 columns]\n"
     ]
    }
   ],
   "source": [
    "# Calcular los valores predichos con el modelo para cada fila en los datos originales\n",
    "datos['Industria_Modelo'] = (coeficientes[0] * datos['Indústria']).astype(int)\n",
    "datos['Construccion_Modelo'] = (coeficientes[1] * datos['Construcció']).astype(int)\n",
    "datos['Servicios_Modelo'] = (coeficientes[2] * datos['Serveis']).astype(int)\n",
    "datos['Total_Modelo'] = datos['Industria_Modelo'] + datos['Construccion_Modelo'] + datos['Servicios_Modelo'] + int(intercepcion)\n",
    "\n",
    "# Calcular la diferencia entre los valores reales y los valores del modelo\n",
    "datos['Dif_Industria'] = datos['Industria_Modelo'] - datos['Indústria']\n",
    "datos['Dif_Construccion'] = datos['Construccion_Modelo'] - datos['Construcció']\n",
    "datos['Dif_Servicios'] = datos['Servicios_Modelo'] - datos['Serveis']\n",
    "datos['Dif_Total'] = datos['Total_Modelo'] - datos['Total']\n",
    "\n",
    "# Determinar si cada valor aumentó o disminuyó\n",
    "datos['Tendencia_Industria'] = np.where(datos['Dif_Industria'] > 0, 'Aumenta', 'Disminuye')\n",
    "datos['Tendencia_Construccion'] = np.where(datos['Dif_Construccion'] > 0, 'Aumenta', 'Disminuye')\n",
    "datos['Tendencia_Servicios'] = np.where(datos['Dif_Servicios'] > 0, 'Aumenta', 'Disminuye')\n",
    "datos['Tendencia_Total'] = np.where(datos['Dif_Total'] > 0, 'Aumenta', 'Disminuye')\n",
    "\n",
    "# Mostrar los resultados\n",
    "columnas_interes = ['Industria_Modelo', 'Indústria', 'Tendencia_Industria',\n",
    "                    'Construccion_Modelo', 'Construcció', 'Tendencia_Construccion',\n",
    "                    'Servicios_Modelo', 'Serveis', 'Tendencia_Servicios',\n",
    "                    'Total_Modelo', 'Total', 'Tendencia_Total']\n",
    "\n",
    "resultados_comparacion = datos[columnas_interes]\n",
    "print(resultados_comparacion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "a9f58b45",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Guardar el DataFrame en un archivo CSV\n",
    "resultados_comparacion.to_csv('resultados_comparacion.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "f463508d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Guardar el DataFrame en un archivo CSV en un directorio específico\n",
    "resultados_comparacion.to_csv('./Downloads/resultados_comparacion.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4f1f11e",
   "metadata": {},
   "source": [
    "## Pronostico: ARIMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "09018cee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\regidor\\anaconda3\\lib\\site-packages\\statsmodels\\graphics\\tsaplots.py:348: FutureWarning:\n",
      "\n",
      "The default method 'yw' can produce PACF values outside of the [-1,1] interval. After 0.13, the default will change tounadjusted Yule-Walker ('ywm'). You can use this method now by setting method='ywm'.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Gráfica ACF\n",
    "plot_acf(datos['Total'], lags=20)\n",
    "plt.show()\n",
    "\n",
    "# Gráfica PACF\n",
    "plot_pacf(datos['Total'], lags=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "5ade6149",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               SARIMAX Results                                \n",
      "==============================================================================\n",
      "Dep. Variable:                  Total   No. Observations:                   73\n",
      "Model:                 ARIMA(5, 1, 0)   Log Likelihood                -518.040\n",
      "Date:                Wed, 04 Oct 2023   AIC                           1048.079\n",
      "Time:                        13:10:19   BIC                           1061.739\n",
      "Sample:                             0   HQIC                          1053.517\n",
      "                                 - 73                                         \n",
      "Covariance Type:                  opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ar.L1          0.0961      0.104      0.923      0.356      -0.108       0.300\n",
      "ar.L2          0.1268      0.085      1.484      0.138      -0.041       0.294\n",
      "ar.L3         -0.0148      0.101     -0.147      0.883      -0.212       0.183\n",
      "ar.L4          0.0501      0.109      0.460      0.646      -0.163       0.264\n",
      "ar.L5         -0.0548      0.100     -0.547      0.584      -0.251       0.141\n",
      "sigma2      1.023e+05    1.8e+04      5.684      0.000    6.71e+04    1.38e+05\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.08   Jarque-Bera (JB):                 9.59\n",
      "Prob(Q):                              0.78   Prob(JB):                         0.01\n",
      "Heteroskedasticity (H):               0.58   Skew:                             0.68\n",
      "Prob(H) (two-sided):                  0.19   Kurtosis:                         4.16\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "\n",
    "# Ajustar el modelo ARIMA\n",
    "# Los parámetros (5,1,0) son un ejemplo, debes ajustarlos según tus datos\n",
    "modelo = ARIMA(datos['Total'], order=(5,1,0))\n",
    "modelo_fit = modelo.fit()\n",
    "\n",
    "# Resumen del modelo\n",
    "print(modelo_fit.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "968ea003",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Fecha  Agricultura  Indústria  Construcció  Serveis  Total   Año  \\\n",
      "0  2023-08-01           70       7145         2405    26350  35970  2023   \n",
      "1  2023-07-01           70       7170         2430    26595  36260  2023   \n",
      "2  2023-06-01           70       7185         2450    26720  36425  2023   \n",
      "3  2023-05-01           65       7180         2435    26975  36660  2023   \n",
      "4  2023-04-01           65       7170         2440    27005  36675  2023   \n",
      "..        ...          ...        ...          ...      ...    ...   ...   \n",
      "68 2013-03-01           60       6631         1833    20336  28860  2013   \n",
      "69 2012-12-01           55       6676         1896    20406  29033  2012   \n",
      "70 2012-09-01           74       6843         1958    20706  29581  2012   \n",
      "71 2012-06-01           76       6948         2023    20542  29589  2012   \n",
      "72 2012-03-01           72       6968         2045    20494  29579  2012   \n",
      "\n",
      "     Media Móvil  Volatilidad    Predicción  ...  Servicios_Modelo  \\\n",
      "0            NaN          NaN  35964.134633  ...             26156   \n",
      "1            NaN          NaN  36259.519742  ...             26399   \n",
      "2   36218.333333          NaN  36420.352688  ...             26523   \n",
      "3   36448.333333          NaN  36652.162148  ...             26776   \n",
      "4   36586.666667          NaN  36677.388774  ...             26806   \n",
      "..           ...          ...           ...  ...               ...   \n",
      "68  28847.666667   840.666024  28858.284285  ...             20186   \n",
      "69  28924.000000   750.955432  29041.287586  ...             20256   \n",
      "70  29158.000000   646.226600  29573.431730  ...             20553   \n",
      "71  29401.000000   506.327979  29586.289746  ...             20391   \n",
      "72  29583.000000   418.203409  29582.566181  ...             20343   \n",
      "\n",
      "    Total_Modelo  Dif_Industria  Dif_Construccion  Dif_Servicios  Dif_Total  \\\n",
      "0          35962             -5               211           -194         -8   \n",
      "1          36257             -5               213           -196         -3   \n",
      "2          36418             -5               215           -197         -7   \n",
      "3          36650             -5               214           -199        -10   \n",
      "4          36675             -5               214           -199          0   \n",
      "..           ...            ...               ...            ...        ...   \n",
      "68         28856             -5               161           -150         -4   \n",
      "69         29039             -5               166           -150          6   \n",
      "70         29571             -5               172           -153        -10   \n",
      "71         29585             -5               178           -151         -4   \n",
      "72         29581             -5               180           -151          2   \n",
      "\n",
      "    Tendencia_Industria  Tendencia_Construccion Tendencia_Servicios  \\\n",
      "0             Disminuye                 Aumenta           Disminuye   \n",
      "1             Disminuye                 Aumenta           Disminuye   \n",
      "2             Disminuye                 Aumenta           Disminuye   \n",
      "3             Disminuye                 Aumenta           Disminuye   \n",
      "4             Disminuye                 Aumenta           Disminuye   \n",
      "..                  ...                     ...                 ...   \n",
      "68            Disminuye                 Aumenta           Disminuye   \n",
      "69            Disminuye                 Aumenta           Disminuye   \n",
      "70            Disminuye                 Aumenta           Disminuye   \n",
      "71            Disminuye                 Aumenta           Disminuye   \n",
      "72            Disminuye                 Aumenta           Disminuye   \n",
      "\n",
      "   Tendencia_Total  \n",
      "0        Disminuye  \n",
      "1        Disminuye  \n",
      "2        Disminuye  \n",
      "3        Disminuye  \n",
      "4        Disminuye  \n",
      "..             ...  \n",
      "68       Disminuye  \n",
      "69         Aumenta  \n",
      "70       Disminuye  \n",
      "71       Disminuye  \n",
      "72         Aumenta  \n",
      "\n",
      "[73 rows x 22 columns]\n"
     ]
    }
   ],
   "source": [
    "print(datos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "bf7b9908",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               SARIMAX Results                                \n",
      "==============================================================================\n",
      "Dep. Variable:                  Total   No. Observations:                   73\n",
      "Model:                 ARIMA(5, 1, 0)   Log Likelihood                -518.040\n",
      "Date:                Wed, 04 Oct 2023   AIC                           1048.079\n",
      "Time:                        13:10:31   BIC                           1061.739\n",
      "Sample:                             0   HQIC                          1053.517\n",
      "                                 - 73                                         \n",
      "Covariance Type:                  opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ar.L1          0.0961      0.104      0.923      0.356      -0.108       0.300\n",
      "ar.L2          0.1268      0.085      1.484      0.138      -0.041       0.294\n",
      "ar.L3         -0.0148      0.101     -0.147      0.883      -0.212       0.183\n",
      "ar.L4          0.0501      0.109      0.460      0.646      -0.163       0.264\n",
      "ar.L5         -0.0548      0.100     -0.547      0.584      -0.251       0.141\n",
      "sigma2      1.023e+05    1.8e+04      5.684      0.000    6.71e+04    1.38e+05\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.08   Jarque-Bera (JB):                 9.59\n",
      "Prob(Q):                              0.78   Prob(JB):                         0.01\n",
      "Heteroskedasticity (H):               0.58   Skew:                             0.68\n",
      "Prob(H) (two-sided):                  0.19   Kurtosis:                         4.16\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "\n",
    "# Ajustar el modelo ARIMA\n",
    "modelo = ARIMA(datos['Total'], order=(5,1,0))\n",
    "modelo_fit = modelo.fit()\n",
    "\n",
    "# Resumen del modelo\n",
    "print(modelo_fit.summary())\n",
    "\n",
    "# Predicciones\n",
    "datos['prediccion'] = modelo_fit.predict(start=1, end=len(datos))\n",
    "\n",
    "# Gráfico de las predicciones\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(datos['Año'], datos['Total'], label='Real')\n",
    "plt.plot(datos['Año'], datos['prediccion'], color='red', label='Predicción')\n",
    "plt.title('Predicción con ARIMA')\n",
    "plt.xlabel('Año')\n",
    "plt.ylabel('Total')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e608bc1",
   "metadata": {},
   "source": [
    "## Causalidad del modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "2139e48b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  Total   R-squared:                       0.586\n",
      "Model:                            OLS   Adj. R-squared:                  0.580\n",
      "Method:                 Least Squares   F-statistic:                     100.4\n",
      "Date:                Wed, 04 Oct 2023   Prob (F-statistic):           3.18e-15\n",
      "Time:                        13:24:10   Log-Likelihood:                -639.27\n",
      "No. Observations:                  73   AIC:                             1283.\n",
      "Df Residuals:                      71   BIC:                             1287.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const      -2.869e+04   6223.759     -4.610      0.000   -4.11e+04   -1.63e+04\n",
      "Indústria      8.8832      0.887     10.018      0.000       7.115      10.651\n",
      "==============================================================================\n",
      "Omnibus:                        3.397   Durbin-Watson:                   0.083\n",
      "Prob(Omnibus):                  0.183   Jarque-Bera (JB):                1.831\n",
      "Skew:                          -0.026   Prob(JB):                        0.400\n",
      "Kurtosis:                       2.226   Cond. No.                     2.39e+05\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 2.39e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "# Seleccionar la variable independiente y agregar una constante al DataFrame\n",
    "X = sm.add_constant(datos['Indústria'])\n",
    "\n",
    "# Ajustar el modelo de regresión OLS (Ordinary Least Squares)\n",
    "modelo = sm.OLS(datos['Total'], X).fit()\n",
    "\n",
    "# Mostrar un resumen del modelo ajustado\n",
    "print(modelo.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "e9682e19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  Total   R-squared:                       0.929\n",
      "Model:                            OLS   Adj. R-squared:                  0.928\n",
      "Method:                 Least Squares   F-statistic:                     930.1\n",
      "Date:                Wed, 04 Oct 2023   Prob (F-statistic):           1.56e-42\n",
      "Time:                        13:24:16   Log-Likelihood:                -574.85\n",
      "No. Observations:                  73   AIC:                             1154.\n",
      "Df Residuals:                      71   BIC:                             1158.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===============================================================================\n",
      "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------\n",
      "const        8096.6107    840.712      9.631      0.000    6420.279    9772.942\n",
      "Construcció    11.7155      0.384     30.498      0.000      10.950      12.481\n",
      "==============================================================================\n",
      "Omnibus:                       32.071   Durbin-Watson:                   0.225\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               62.793\n",
      "Skew:                          -1.579   Prob(JB):                     2.32e-14\n",
      "Kurtosis:                       6.267   Cond. No.                     2.44e+04\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 2.44e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "# Seleccionar la variable independiente y agregar una constante al DataFrame\n",
    "X = sm.add_constant(datos['Construcció'])\n",
    "\n",
    "# Ajustar el modelo de regresión OLS (Ordinary Least Squares)\n",
    "modelo = sm.OLS(datos['Total'], X).fit()\n",
    "\n",
    "# Mostrar un resumen del modelo ajustado\n",
    "print(modelo.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "08bc8bb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  Total   R-squared:                       0.994\n",
      "Model:                            OLS   Adj. R-squared:                  0.994\n",
      "Method:                 Least Squares   F-statistic:                 1.210e+04\n",
      "Date:                Wed, 04 Oct 2023   Prob (F-statistic):           4.63e-81\n",
      "Time:                        13:24:25   Log-Likelihood:                -483.67\n",
      "No. Observations:                  73   AIC:                             971.3\n",
      "Df Residuals:                      71   BIC:                             975.9\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const       5256.1814    258.870     20.304      0.000    4740.009    5772.354\n",
      "Serveis        1.1640      0.011    110.004      0.000       1.143       1.185\n",
      "==============================================================================\n",
      "Omnibus:                        2.027   Durbin-Watson:                   0.178\n",
      "Prob(Omnibus):                  0.363   Jarque-Bera (JB):                1.949\n",
      "Skew:                           0.321   Prob(JB):                        0.377\n",
      "Kurtosis:                       2.522   Cond. No.                     2.92e+05\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 2.92e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "# Seleccionar la variable independiente y agregar una constante al DataFrame\n",
    "X = sm.add_constant(datos['Serveis'])\n",
    "\n",
    "# Ajustar el modelo de regresión OLS (Ordinary Least Squares)\n",
    "modelo = sm.OLS(datos['Total'], X).fit()\n",
    "\n",
    "# Mostrar un resumen del modelo ajustado\n",
    "print(modelo.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbcc0a4a",
   "metadata": {},
   "source": [
    "Para un análisis más profundo de la causalidad, podríamos considerar métodos más avanzados y específicos, como los modelos de ecuaciones estructurales, pruebas de causalidad de Granger, o métodos basados en la teoría de la causalidad de Judea Pearl, que incluyen gráficos causales y ecuaciones estructurales.\n",
    "\n",
    "Para la prueba de causalidad de Granger, por ejemplo, el código se vería algo así:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "0c301518",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=0.0255  , p=0.8737  , df_denom=69, df_num=1\n",
      "ssr based chi2 test:   chi2=0.0266  , p=0.8705  , df=1\n",
      "likelihood ratio test: chi2=0.0266  , p=0.8705  , df=1\n",
      "parameter F test:         F=0.0255  , p=0.8737  , df_denom=69, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=0.1302  , p=0.8781  , df_denom=66, df_num=2\n",
      "ssr based chi2 test:   chi2=0.2802  , p=0.8693  , df=2\n",
      "likelihood ratio test: chi2=0.2797  , p=0.8695  , df=2\n",
      "parameter F test:         F=0.1302  , p=0.8781  , df_denom=66, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=1.1466  , p=0.3373  , df_denom=63, df_num=3\n",
      "ssr based chi2 test:   chi2=3.8221  , p=0.2813  , df=3\n",
      "likelihood ratio test: chi2=3.7214  , p=0.2932  , df=3\n",
      "parameter F test:         F=1.1466  , p=0.3373  , df_denom=63, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=0.8074  , p=0.5254  , df_denom=60, df_num=4\n",
      "ssr based chi2 test:   chi2=3.7139  , p=0.4461  , df=4\n",
      "likelihood ratio test: chi2=3.6174  , p=0.4602  , df=4\n",
      "parameter F test:         F=0.8074  , p=0.5254  , df_denom=60, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=0.5339  , p=0.7497  , df_denom=57, df_num=5\n",
      "ssr based chi2 test:   chi2=3.1845  , p=0.6716  , df=5\n",
      "likelihood ratio test: chi2=3.1122  , p=0.6827  , df=5\n",
      "parameter F test:         F=0.5339  , p=0.7497  , df_denom=57, df_num=5\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 6\n",
      "ssr based F test:         F=0.7191  , p=0.6359  , df_denom=54, df_num=6\n",
      "ssr based chi2 test:   chi2=5.3530  , p=0.4994  , df=6\n",
      "likelihood ratio test: chi2=5.1500  , p=0.5247  , df=6\n",
      "parameter F test:         F=0.7191  , p=0.6359  , df_denom=54, df_num=6\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 7\n",
      "ssr based F test:         F=1.2802  , p=0.2788  , df_denom=51, df_num=7\n",
      "ssr based chi2 test:   chi2=11.5972 , p=0.1146  , df=7\n",
      "likelihood ratio test: chi2=10.6839 , p=0.1530  , df=7\n",
      "parameter F test:         F=1.2802  , p=0.2788  , df_denom=51, df_num=7\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 8\n",
      "ssr based F test:         F=1.0952  , p=0.3830  , df_denom=48, df_num=8\n",
      "ssr based chi2 test:   chi2=11.8651 , p=0.1573  , df=8\n",
      "likelihood ratio test: chi2=10.8982 , p=0.2075  , df=8\n",
      "parameter F test:         F=1.0952  , p=0.3830  , df_denom=48, df_num=8\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 9\n",
      "ssr based F test:         F=1.2198  , p=0.3072  , df_denom=45, df_num=9\n",
      "ssr based chi2 test:   chi2=15.6131 , p=0.0754  , df=9\n",
      "likelihood ratio test: chi2=13.9709 , p=0.1234  , df=9\n",
      "parameter F test:         F=1.2198  , p=0.3072  , df_denom=45, df_num=9\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 10\n",
      "ssr based F test:         F=0.9601  , p=0.4911  , df_denom=42, df_num=10\n",
      "ssr based chi2 test:   chi2=14.4019 , p=0.1554  , df=10\n",
      "likelihood ratio test: chi2=12.9702 , p=0.2253  , df=10\n",
      "parameter F test:         F=0.9601  , p=0.4911  , df_denom=42, df_num=10\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 11\n",
      "ssr based F test:         F=0.9206  , p=0.5308  , df_denom=39, df_num=11\n",
      "ssr based chi2 test:   chi2=16.0986 , p=0.1375  , df=11\n",
      "likelihood ratio test: chi2=14.3119 , p=0.2162  , df=11\n",
      "parameter F test:         F=0.9206  , p=0.5308  , df_denom=39, df_num=11\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 12\n",
      "ssr based F test:         F=0.8102  , p=0.6384  , df_denom=36, df_num=12\n",
      "ssr based chi2 test:   chi2=16.4749 , p=0.1704  , df=12\n",
      "likelihood ratio test: chi2=14.5839 , p=0.2650  , df=12\n",
      "parameter F test:         F=0.8102  , p=0.6384  , df_denom=36, df_num=12\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import grangercausalitytests\n",
    "\n",
    "# Suponiendo que queremos probar si 'Indústria' causa 'Total'\n",
    "max_lag = 12\n",
    "test = 'ssr_chi2test'\n",
    "data = datos[['Indústria', 'Total']]\n",
    "\n",
    "# Realizar la prueba de causalidad de Granger\n",
    "granger_test_result = grangercausalitytests(data, max_lag, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "537256c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=3.1185  , p=0.0818  , df_denom=69, df_num=1\n",
      "ssr based chi2 test:   chi2=3.2541  , p=0.0712  , df=1\n",
      "likelihood ratio test: chi2=3.1827  , p=0.0744  , df=1\n",
      "parameter F test:         F=3.1185  , p=0.0818  , df_denom=69, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=0.5818  , p=0.5617  , df_denom=66, df_num=2\n",
      "ssr based chi2 test:   chi2=1.2518  , p=0.5348  , df=2\n",
      "likelihood ratio test: chi2=1.2409  , p=0.5377  , df=2\n",
      "parameter F test:         F=0.5818  , p=0.5617  , df_denom=66, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=1.5939  , p=0.1997  , df_denom=63, df_num=3\n",
      "ssr based chi2 test:   chi2=5.3130  , p=0.1503  , df=3\n",
      "likelihood ratio test: chi2=5.1210  , p=0.1631  , df=3\n",
      "parameter F test:         F=1.5939  , p=0.1997  , df_denom=63, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=1.3466  , p=0.2633  , df_denom=60, df_num=4\n",
      "ssr based chi2 test:   chi2=6.1943  , p=0.1851  , df=4\n",
      "likelihood ratio test: chi2=5.9319  , p=0.2043  , df=4\n",
      "parameter F test:         F=1.3466  , p=0.2633  , df_denom=60, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=0.6157  , p=0.6883  , df_denom=57, df_num=5\n",
      "ssr based chi2 test:   chi2=3.6724  , p=0.5975  , df=5\n",
      "likelihood ratio test: chi2=3.5766  , p=0.6118  , df=5\n",
      "parameter F test:         F=0.6157  , p=0.6883  , df_denom=57, df_num=5\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 6\n",
      "ssr based F test:         F=0.6320  , p=0.7040  , df_denom=54, df_num=6\n",
      "ssr based chi2 test:   chi2=4.7050  , p=0.5822  , df=6\n",
      "likelihood ratio test: chi2=4.5471  , p=0.6031  , df=6\n",
      "parameter F test:         F=0.6320  , p=0.7040  , df_denom=54, df_num=6\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 7\n",
      "ssr based F test:         F=0.5354  , p=0.8036  , df_denom=51, df_num=7\n",
      "ssr based chi2 test:   chi2=4.8498  , p=0.6783  , df=7\n",
      "likelihood ratio test: chi2=4.6799  , p=0.6990  , df=7\n",
      "parameter F test:         F=0.5354  , p=0.8036  , df_denom=51, df_num=7\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 8\n",
      "ssr based F test:         F=0.4823  , p=0.8627  , df_denom=48, df_num=8\n",
      "ssr based chi2 test:   chi2=5.2247  , p=0.7333  , df=8\n",
      "likelihood ratio test: chi2=5.0254  , p=0.7549  , df=8\n",
      "parameter F test:         F=0.4823  , p=0.8627  , df_denom=48, df_num=8\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 9\n",
      "ssr based F test:         F=0.2530  , p=0.9837  , df_denom=45, df_num=9\n",
      "ssr based chi2 test:   chi2=3.2383  , p=0.9541  , df=9\n",
      "likelihood ratio test: chi2=3.1591  , p=0.9577  , df=9\n",
      "parameter F test:         F=0.2530  , p=0.9837  , df_denom=45, df_num=9\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 10\n",
      "ssr based F test:         F=0.4824  , p=0.8920  , df_denom=42, df_num=10\n",
      "ssr based chi2 test:   chi2=7.2364  , p=0.7030  , df=10\n",
      "likelihood ratio test: chi2=6.8501  , p=0.7395  , df=10\n",
      "parameter F test:         F=0.4824  , p=0.8920  , df_denom=42, df_num=10\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 11\n",
      "ssr based F test:         F=0.5081  , p=0.8861  , df_denom=39, df_num=11\n",
      "ssr based chi2 test:   chi2=8.8860  , p=0.6324  , df=11\n",
      "likelihood ratio test: chi2=8.3042  , p=0.6858  , df=11\n",
      "parameter F test:         F=0.5081  , p=0.8861  , df_denom=39, df_num=11\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 12\n",
      "ssr based F test:         F=0.6880  , p=0.7517  , df_denom=36, df_num=12\n",
      "ssr based chi2 test:   chi2=13.9885 , p=0.3014  , df=12\n",
      "likelihood ratio test: chi2=12.5941 , p=0.3992  , df=12\n",
      "parameter F test:         F=0.6880  , p=0.7517  , df_denom=36, df_num=12\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import grangercausalitytests\n",
    "\n",
    "# Suponiendo que queremos probar si 'Indústria' causa 'Total'\n",
    "max_lag = 12\n",
    "test = 'ssr_chi2test'\n",
    "data = datos[['Construcció', 'Total']]\n",
    "\n",
    "# Realizar la prueba de causalidad de Granger\n",
    "granger_test_result = grangercausalitytests(data, max_lag, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "cdb03880",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=1.2806  , p=0.2617  , df_denom=69, df_num=1\n",
      "ssr based chi2 test:   chi2=1.3363  , p=0.2477  , df=1\n",
      "likelihood ratio test: chi2=1.3241  , p=0.2499  , df=1\n",
      "parameter F test:         F=1.2806  , p=0.2617  , df_denom=69, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=0.6773  , p=0.5115  , df_denom=66, df_num=2\n",
      "ssr based chi2 test:   chi2=1.4572  , p=0.4826  , df=2\n",
      "likelihood ratio test: chi2=1.4425  , p=0.4861  , df=2\n",
      "parameter F test:         F=0.6773  , p=0.5115  , df_denom=66, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=0.5182  , p=0.6713  , df_denom=63, df_num=3\n",
      "ssr based chi2 test:   chi2=1.7273  , p=0.6309  , df=3\n",
      "likelihood ratio test: chi2=1.7063  , p=0.6355  , df=3\n",
      "parameter F test:         F=0.5182  , p=0.6713  , df_denom=63, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=0.7001  , p=0.5949  , df_denom=60, df_num=4\n",
      "ssr based chi2 test:   chi2=3.2204  , p=0.5216  , df=4\n",
      "likelihood ratio test: chi2=3.1475  , p=0.5335  , df=4\n",
      "parameter F test:         F=0.7001  , p=0.5949  , df_denom=60, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=1.6546  , p=0.1605  , df_denom=57, df_num=5\n",
      "ssr based chi2 test:   chi2=9.8694  , p=0.0790  , df=5\n",
      "likelihood ratio test: chi2=9.2158  , p=0.1008  , df=5\n",
      "parameter F test:         F=1.6546  , p=0.1605  , df_denom=57, df_num=5\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 6\n",
      "ssr based F test:         F=1.3607  , p=0.2472  , df_denom=54, df_num=6\n",
      "ssr based chi2 test:   chi2=10.1296 , p=0.1193  , df=6\n",
      "likelihood ratio test: chi2=9.4332  , p=0.1506  , df=6\n",
      "parameter F test:         F=1.3607  , p=0.2472  , df_denom=54, df_num=6\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 7\n",
      "ssr based F test:         F=1.1523  , p=0.3464  , df_denom=51, df_num=7\n",
      "ssr based chi2 test:   chi2=10.4388 , p=0.1650  , df=7\n",
      "likelihood ratio test: chi2=9.6912  , p=0.2068  , df=7\n",
      "parameter F test:         F=1.1523  , p=0.3464  , df_denom=51, df_num=7\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 8\n",
      "ssr based F test:         F=1.0802  , p=0.3928  , df_denom=48, df_num=8\n",
      "ssr based chi2 test:   chi2=11.7026 , p=0.1650  , df=8\n",
      "likelihood ratio test: chi2=10.7606 , p=0.2156  , df=8\n",
      "parameter F test:         F=1.0802  , p=0.3928  , df_denom=48, df_num=8\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 9\n",
      "ssr based F test:         F=0.9175  , p=0.5189  , df_denom=45, df_num=9\n",
      "ssr based chi2 test:   chi2=11.7444 , p=0.2281  , df=9\n",
      "likelihood ratio test: chi2=10.7828 , p=0.2909  , df=9\n",
      "parameter F test:         F=0.9175  , p=0.5189  , df_denom=45, df_num=9\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 10\n",
      "ssr based F test:         F=2.2556  , p=0.0325  , df_denom=42, df_num=10\n",
      "ssr based chi2 test:   chi2=33.8335 , p=0.0002  , df=10\n",
      "likelihood ratio test: chi2=27.0811 , p=0.0025  , df=10\n",
      "parameter F test:         F=2.2556  , p=0.0325  , df_denom=42, df_num=10\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 11\n",
      "ssr based F test:         F=1.7232  , p=0.1042  , df_denom=39, df_num=11\n",
      "ssr based chi2 test:   chi2=30.1336 , p=0.0015  , df=11\n",
      "likelihood ratio test: chi2=24.5585 , p=0.0106  , df=11\n",
      "parameter F test:         F=1.7232  , p=0.1042  , df_denom=39, df_num=11\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 12\n",
      "ssr based F test:         F=1.5030  , p=0.1684  , df_denom=36, df_num=12\n",
      "ssr based chi2 test:   chi2=30.5614 , p=0.0023  , df=12\n",
      "likelihood ratio test: chi2=24.7743 , p=0.0159  , df=12\n",
      "parameter F test:         F=1.5030  , p=0.1684  , df_denom=36, df_num=12\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import grangercausalitytests\n",
    "\n",
    "# Suponiendo que queremos probar si 'Indústria' causa 'Total'\n",
    "max_lag = 12\n",
    "test = 'ssr_chi2test'\n",
    "data = datos[['Serveis', 'Total']]\n",
    "\n",
    "# Realizar la prueba de causalidad de Granger\n",
    "granger_test_result = grangercausalitytests(data, max_lag, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "e3ceda8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=1.6012  , p=0.2100  , df_denom=69, df_num=1\n",
      "ssr based chi2 test:   chi2=1.6708  , p=0.1962  , df=1\n",
      "likelihood ratio test: chi2=1.6517  , p=0.1987  , df=1\n",
      "parameter F test:         F=1.6012  , p=0.2100  , df_denom=69, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=0.8233  , p=0.4434  , df_denom=66, df_num=2\n",
      "ssr based chi2 test:   chi2=1.7714  , p=0.4124  , df=2\n",
      "likelihood ratio test: chi2=1.7497  , p=0.4169  , df=2\n",
      "parameter F test:         F=0.8233  , p=0.4434  , df_denom=66, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=1.0372  , p=0.3823  , df_denom=63, df_num=3\n",
      "ssr based chi2 test:   chi2=3.4573  , p=0.3263  , df=3\n",
      "likelihood ratio test: chi2=3.3747  , p=0.3374  , df=3\n",
      "parameter F test:         F=1.0372  , p=0.3823  , df_denom=63, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=0.7561  , p=0.5579  , df_denom=60, df_num=4\n",
      "ssr based chi2 test:   chi2=3.4783  , p=0.4812  , df=4\n",
      "likelihood ratio test: chi2=3.3934  , p=0.4943  , df=4\n",
      "parameter F test:         F=0.7561  , p=0.5579  , df_denom=60, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=0.8311  , p=0.5330  , df_denom=57, df_num=5\n",
      "ssr based chi2 test:   chi2=4.9574  , p=0.4211  , df=5\n",
      "likelihood ratio test: chi2=4.7850  , p=0.4427  , df=5\n",
      "parameter F test:         F=0.8311  , p=0.5330  , df_denom=57, df_num=5\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 6\n",
      "ssr based F test:         F=0.7506  , p=0.6117  , df_denom=54, df_num=6\n",
      "ssr based chi2 test:   chi2=5.5875  , p=0.4710  , df=6\n",
      "likelihood ratio test: chi2=5.3667  , p=0.4977  , df=6\n",
      "parameter F test:         F=0.7506  , p=0.6117  , df_denom=54, df_num=6\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 7\n",
      "ssr based F test:         F=1.1169  , p=0.3671  , df_denom=51, df_num=7\n",
      "ssr based chi2 test:   chi2=10.1182 , p=0.1820  , df=7\n",
      "likelihood ratio test: chi2=9.4137  , p=0.2243  , df=7\n",
      "parameter F test:         F=1.1169  , p=0.3671  , df_denom=51, df_num=7\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 8\n",
      "ssr based F test:         F=0.9473  , p=0.4876  , df_denom=48, df_num=8\n",
      "ssr based chi2 test:   chi2=10.2619 , p=0.2471  , df=8\n",
      "likelihood ratio test: chi2=9.5281  , p=0.2997  , df=8\n",
      "parameter F test:         F=0.9473  , p=0.4876  , df_denom=48, df_num=8\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 9\n",
      "ssr based F test:         F=0.9177  , p=0.5188  , df_denom=45, df_num=9\n",
      "ssr based chi2 test:   chi2=11.7462 , p=0.2280  , df=9\n",
      "likelihood ratio test: chi2=10.7843 , p=0.2908  , df=9\n",
      "parameter F test:         F=0.9177  , p=0.5188  , df_denom=45, df_num=9\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 10\n",
      "ssr based F test:         F=0.7041  , p=0.7152  , df_denom=42, df_num=10\n",
      "ssr based chi2 test:   chi2=10.5612 , p=0.3927  , df=10\n",
      "likelihood ratio test: chi2=9.7639  , p=0.4614  , df=10\n",
      "parameter F test:         F=0.7041  , p=0.7152  , df_denom=42, df_num=10\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 11\n",
      "ssr based F test:         F=0.5925  , p=0.8231  , df_denom=39, df_num=11\n",
      "ssr based chi2 test:   chi2=10.3614 , p=0.4982  , df=11\n",
      "likelihood ratio test: chi2=9.5814  , p=0.5684  , df=11\n",
      "parameter F test:         F=0.5925  , p=0.8231  , df_denom=39, df_num=11\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 12\n",
      "ssr based F test:         F=0.5321  , p=0.8792  , df_denom=36, df_num=12\n",
      "ssr based chi2 test:   chi2=10.8198 , p=0.5444  , df=12\n",
      "likelihood ratio test: chi2=9.9604  , p=0.6194  , df=12\n",
      "parameter F test:         F=0.5321  , p=0.8792  , df_denom=36, df_num=12\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import grangercausalitytests\n",
    "\n",
    "# Suponiendo que queremos probar si 'Indústria' causa 'Total'\n",
    "max_lag = 12\n",
    "test = 'ssr_chi2test'\n",
    "data = datos[['Indústria', 'Construcció']]\n",
    "\n",
    "# Realizar la prueba de causalidad de Granger\n",
    "granger_test_result = grangercausalitytests(data, max_lag, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "b43946a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=0.0028  , p=0.9579  , df_denom=69, df_num=1\n",
      "ssr based chi2 test:   chi2=0.0029  , p=0.9569  , df=1\n",
      "likelihood ratio test: chi2=0.0029  , p=0.9569  , df=1\n",
      "parameter F test:         F=0.0028  , p=0.9579  , df_denom=69, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=0.1496  , p=0.8613  , df_denom=66, df_num=2\n",
      "ssr based chi2 test:   chi2=0.3219  , p=0.8513  , df=2\n",
      "likelihood ratio test: chi2=0.3212  , p=0.8516  , df=2\n",
      "parameter F test:         F=0.1496  , p=0.8613  , df_denom=66, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=1.1961  , p=0.3185  , df_denom=63, df_num=3\n",
      "ssr based chi2 test:   chi2=3.9871  , p=0.2629  , df=3\n",
      "likelihood ratio test: chi2=3.8777  , p=0.2750  , df=3\n",
      "parameter F test:         F=1.1961  , p=0.3185  , df_denom=63, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=0.8468  , p=0.5012  , df_denom=60, df_num=4\n",
      "ssr based chi2 test:   chi2=3.8952  , p=0.4204  , df=4\n",
      "likelihood ratio test: chi2=3.7893  , p=0.4353  , df=4\n",
      "parameter F test:         F=0.8468  , p=0.5012  , df_denom=60, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=0.5194  , p=0.7605  , df_denom=57, df_num=5\n",
      "ssr based chi2 test:   chi2=3.0983  , p=0.6848  , df=5\n",
      "likelihood ratio test: chi2=3.0298  , p=0.6954  , df=5\n",
      "parameter F test:         F=0.5194  , p=0.7605  , df_denom=57, df_num=5\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 6\n",
      "ssr based F test:         F=0.6951  , p=0.6545  , df_denom=54, df_num=6\n",
      "ssr based chi2 test:   chi2=5.1749  , p=0.5216  , df=6\n",
      "likelihood ratio test: chi2=4.9848  , p=0.5458  , df=6\n",
      "parameter F test:         F=0.6951  , p=0.6545  , df_denom=54, df_num=6\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 7\n",
      "ssr based F test:         F=1.2903  , p=0.2740  , df_denom=51, df_num=7\n",
      "ssr based chi2 test:   chi2=11.6889 , p=0.1113  , df=7\n",
      "likelihood ratio test: chi2=10.7618 , p=0.1493  , df=7\n",
      "parameter F test:         F=1.2903  , p=0.2740  , df_denom=51, df_num=7\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 8\n",
      "ssr based F test:         F=1.0920  , p=0.3851  , df_denom=48, df_num=8\n",
      "ssr based chi2 test:   chi2=11.8296 , p=0.1590  , df=8\n",
      "likelihood ratio test: chi2=10.8682 , p=0.2093  , df=8\n",
      "parameter F test:         F=1.0920  , p=0.3851  , df_denom=48, df_num=8\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 9\n",
      "ssr based F test:         F=1.3074  , p=0.2600  , df_denom=45, df_num=9\n",
      "ssr based chi2 test:   chi2=16.7343 , p=0.0530  , df=9\n",
      "likelihood ratio test: chi2=14.8660 , p=0.0947  , df=9\n",
      "parameter F test:         F=1.3074  , p=0.2600  , df_denom=45, df_num=9\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 10\n",
      "ssr based F test:         F=1.0442  , p=0.4250  , df_denom=42, df_num=10\n",
      "ssr based chi2 test:   chi2=15.6635 , p=0.1097  , df=10\n",
      "likelihood ratio test: chi2=13.9888 , p=0.1735  , df=10\n",
      "parameter F test:         F=1.0442  , p=0.4250  , df_denom=42, df_num=10\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 11\n",
      "ssr based F test:         F=1.0310  , p=0.4390  , df_denom=39, df_num=11\n",
      "ssr based chi2 test:   chi2=18.0300 , p=0.0809  , df=11\n",
      "likelihood ratio test: chi2=15.8265 , p=0.1477  , df=11\n",
      "parameter F test:         F=1.0310  , p=0.4390  , df_denom=39, df_num=11\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 12\n",
      "ssr based F test:         F=0.9125  , p=0.5443  , df_denom=36, df_num=12\n",
      "ssr based chi2 test:   chi2=18.5551 , p=0.0998  , df=12\n",
      "likelihood ratio test: chi2=16.2001 , p=0.1822  , df=12\n",
      "parameter F test:         F=0.9125  , p=0.5443  , df_denom=36, df_num=12\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import grangercausalitytests\n",
    "\n",
    "# Suponiendo que queremos probar si 'Indústria' causa 'Total'\n",
    "max_lag = 12\n",
    "test = 'ssr_chi2test'\n",
    "data = datos[['Indústria', 'Serveis']]\n",
    "\n",
    "# Realizar la prueba de causalidad de Granger\n",
    "granger_test_result = grangercausalitytests(data, max_lag, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "3a86d1ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=3.8238  , p=0.0546  , df_denom=69, df_num=1\n",
      "ssr based chi2 test:   chi2=3.9900  , p=0.0458  , df=1\n",
      "likelihood ratio test: chi2=3.8834  , p=0.0488  , df=1\n",
      "parameter F test:         F=3.8238  , p=0.0546  , df_denom=69, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=2.1598  , p=0.1234  , df_denom=66, df_num=2\n",
      "ssr based chi2 test:   chi2=4.6468  , p=0.0979  , df=2\n",
      "likelihood ratio test: chi2=4.5010  , p=0.1053  , df=2\n",
      "parameter F test:         F=2.1598  , p=0.1234  , df_denom=66, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=2.8487  , p=0.0445  , df_denom=63, df_num=3\n",
      "ssr based chi2 test:   chi2=9.4957  , p=0.0234  , df=3\n",
      "likelihood ratio test: chi2=8.9045  , p=0.0306  , df=3\n",
      "parameter F test:         F=2.8487  , p=0.0445  , df_denom=63, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=1.9761  , p=0.1097  , df_denom=60, df_num=4\n",
      "ssr based chi2 test:   chi2=9.0902  , p=0.0589  , df=4\n",
      "likelihood ratio test: chi2=8.5393  , p=0.0737  , df=4\n",
      "parameter F test:         F=1.9761  , p=0.1097  , df_denom=60, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=1.5129  , p=0.2002  , df_denom=57, df_num=5\n",
      "ssr based chi2 test:   chi2=9.0246  , p=0.1081  , df=5\n",
      "likelihood ratio test: chi2=8.4740  , p=0.1320  , df=5\n",
      "parameter F test:         F=1.5129  , p=0.2002  , df_denom=57, df_num=5\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 6\n",
      "ssr based F test:         F=1.3661  , p=0.2450  , df_denom=54, df_num=6\n",
      "ssr based chi2 test:   chi2=10.1700 , p=0.1177  , df=6\n",
      "likelihood ratio test: chi2=9.4683  , p=0.1489  , df=6\n",
      "parameter F test:         F=1.3661  , p=0.2450  , df_denom=54, df_num=6\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 7\n",
      "ssr based F test:         F=1.1171  , p=0.3671  , df_denom=51, df_num=7\n",
      "ssr based chi2 test:   chi2=10.1193 , p=0.1819  , df=7\n",
      "likelihood ratio test: chi2=9.4147  , p=0.2242  , df=7\n",
      "parameter F test:         F=1.1171  , p=0.3671  , df_denom=51, df_num=7\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 8\n",
      "ssr based F test:         F=0.9543  , p=0.4823  , df_denom=48, df_num=8\n",
      "ssr based chi2 test:   chi2=10.3378 , p=0.2421  , df=8\n",
      "likelihood ratio test: chi2=9.5937  , p=0.2947  , df=8\n",
      "parameter F test:         F=0.9543  , p=0.4823  , df_denom=48, df_num=8\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 9\n",
      "ssr based F test:         F=0.8521  , p=0.5735  , df_denom=45, df_num=9\n",
      "ssr based chi2 test:   chi2=10.9063 , p=0.2822  , df=9\n",
      "likelihood ratio test: chi2=10.0707 , p=0.3448  , df=9\n",
      "parameter F test:         F=0.8521  , p=0.5735  , df_denom=45, df_num=9\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 10\n",
      "ssr based F test:         F=1.2903  , p=0.2670  , df_denom=42, df_num=10\n",
      "ssr based chi2 test:   chi2=19.3544 , p=0.0360  , df=10\n",
      "likelihood ratio test: chi2=16.8775 , p=0.0771  , df=10\n",
      "parameter F test:         F=1.2903  , p=0.2670  , df_denom=42, df_num=10\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 11\n",
      "ssr based F test:         F=1.6667  , p=0.1183  , df_denom=39, df_num=11\n",
      "ssr based chi2 test:   chi2=29.1451 , p=0.0022  , df=11\n",
      "likelihood ratio test: chi2=23.8898 , p=0.0132  , df=11\n",
      "parameter F test:         F=1.6667  , p=0.1183  , df_denom=39, df_num=11\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 12\n",
      "ssr based F test:         F=1.4455  , p=0.1911  , df_denom=36, df_num=12\n",
      "ssr based chi2 test:   chi2=29.3920 , p=0.0034  , df=12\n",
      "likelihood ratio test: chi2=23.9902 , p=0.0204  , df=12\n",
      "parameter F test:         F=1.4455  , p=0.1911  , df_denom=36, df_num=12\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.tsa.stattools import grangercausalitytests\n",
    "\n",
    "# Suponiendo que queremos probar si 'Indústria' causa 'Total'\n",
    "max_lag = 12\n",
    "test = 'ssr_chi2test'\n",
    "data = datos[['Serveis', 'Construcció']]\n",
    "\n",
    "# Realizar la prueba de causalidad de Granger\n",
    "granger_test_result = grangercausalitytests(data, max_lag, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "541b0ada",
   "metadata": {},
   "source": [
    "En este código, max_lag es el número máximo de lags que quieres incluir en tu modelo. La función grangercausalitytests realiza la prueba de causalidad de Granger para cada lag hasta max_lag y te dará un resumen de los resultados."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be2e2176",
   "metadata": {},
   "source": [
    "2. Pruebas de Hipótesis\n",
    "Podemos usar la prueba t para comparar las medias de dos grupos. Por ejemplo, podríamos querer comparar los valores totales antes y después de un año específico."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "4e0b31a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estadístico t: -9.241847111611136\n",
      "Valor p: 8.403331855512596e-14\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import ttest_ind\n",
    "\n",
    "# Dividir los datos en dos grupos\n",
    "grupo1 = datos[datos['Año'] < 2020]['Total']\n",
    "grupo2 = datos[datos['Año'] >= 2020]['Total']\n",
    "\n",
    "# Realizar una prueba t\n",
    "t_stat, p_valor = ttest_ind(grupo1, grupo2)\n",
    "\n",
    "print(f\"Estadístico t: {t_stat}\")\n",
    "print(f\"Valor p: {p_valor}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1744acd6",
   "metadata": {},
   "source": [
    "Los resultados que obtuviste indican que hay una diferencia estadísticamente significativa entre los dos grupos que comparaste, ya que el valor p es extremadamente pequeño (8.403331855512596e-14).\n",
    "\n",
    "Interpretación:\n",
    "Estadístico t: El valor t de -9.24 indica la diferencia entre las medias de los dos grupos. Un valor t negativo sugiere que la media del segundo grupo (después del año 2020 en tu caso) es mayor que la del primer grupo (antes del año 2020).\n",
    "\n",
    "Valor p: Un valor p muy pequeño (menor que 0.05) es una indicación clara de que podemos rechazar la hipótesis nula. En este contexto, la hipótesis nula es que no hay diferencia significativa entre los grupos. Un valor p tan pequeño nos dice que hay una diferencia muy significativa entre los valores totales antes y después del año 2020.\n",
    "\n",
    "Conclusión:\n",
    "Puedes concluir con confianza que hay una diferencia significativa en los valores totales de afiliación antes y después del año 2020. Esto podría deberse a varios factores que podrías investigar más a fondo, como cambios en la economía, políticas gubernamentales, eventos mundiales, etc.\n",
    "\n",
    "Para entender completamente la causalidad y los factores de influencia, podrías considerar realizar un análisis más profundo, incluyendo un análisis de regresión múltiple para entender cómo varias variables independientes afectan a la variable dependiente (Total) simultáneamente, y analizar los eventos específicos o cambios durante ese período de tiempo que podrían haber influido en la afiliación."
   ]
  }
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