Predecir_el_valor_de_Shiba_Inu_EUR.ipynb

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      "source": [
        "# Prophet - Predecir el valor de Shiba Inu EUR\n"
      ],
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      }
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      "cell_type": "markdown",
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        "Prophet es una de las librerías más avanzadas para predecir series temporales desarrollada por Facebook. Te enseñaremos a como entrenar un modelo con Prophet, a optimizarlo y a utilizarlo para realizar predicciones futuras. En este ejercicio vamos a practicar a predecir el valor de Shiba Inu, una criptomoneda. "
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        "#Dependencias de Prophet\n",
        "!pip install pystan==2.19.1.1 --quiet\n",
        "!pip install cmdstanpy --quiet\n",
        "# Instalamos el modelo predictivo Prophet\n",
        "!pip install fbprophet --quiet\n",
        "# De aquí vamos a descargar los datos\n",
        "!pip install yfinance --quiet"
      ],
      "execution_count": null,
      "outputs": []
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      "source": [
        "#Importamos las dependencias\n",
        "import pandas as pd\n",
        "import yfinance as yf\n",
        "from datetime import datetime\n",
        "from datetime import timedelta\n",
        "import plotly.graph_objects as go\n",
        "from fbprophet import Prophet\n",
        "from fbprophet.plot import plot_plotly, plot_components_plotly\n",
        "import warnings\n",
        "\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "pd.options.display.float_format = '${:,.2f}'.format"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h3 id=\"version\">Cargar histórico de datos</h3>\n"
      ],
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    {
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      "source": [
        "<p>\n",
        "    Para descargarnos la serie temporal de Shiba Inu lo vamos a hacer desde Yahoo Finance. En concreto vamos a descargar el histórico desde 2016 hasta la actualidad.\n",
        "</p>\n",
        "\n"
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      "source": [
        "# Fecha de inicio es un string 2016-01-01 y fecha de fin (hoy) en formato texto '%Y-%m-%d'\n",
        "today = '2022-11-09'\n",
        "start_date = '2016-01-01'\n",
        "# Descargar el dataframe\n",
        "btc_df = yf.download('SHIB-EUR',start_date, today).reset_index()\n",
        "\n",
        "btc_df.tail()"
      ],
      "execution_count": null,
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            "\r[*********************100%***********************]  1 of 1 completed\n"
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        "btc_df.isnull().sum()"
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            "text/plain": [
              "Date         0\n",
              "Open         0\n",
              "High         0\n",
              "Low          0\n",
              "Close        0\n",
              "Adj Close    0\n",
              "Volume       0\n",
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      "cell_type": "code",
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        "id": "_940Bbrdtpme"
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      "source": [
        "# Validamos que no hay datos vacíos\n"
      ],
      "execution_count": null,
      "outputs": []
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      "source": [
        "# TAREA: Filtramos el dataframe para quedarnos solo las columnas \"Date\" y \"Open\"\n",
        "# Date será la fecha del valor y Open el valor del Shiba en ese momento\n",
        "df = btc_df[['Date','Open']]\n",
        "\n",
        "# Valores que espera el prophet\n",
        "# Eje X: Indíce llamado ds\n",
        "# Eje Y: Serie a predecir llamada y\n",
        "# TAREA: Crear un diccionario con clave \"Date\" y valor \"ds\"\n",
        "# Y otra clave \"Open\" y valor \"y\"\n",
        "new_names = {\n",
        "    \"Date\": \"ds\", \n",
        "    \"Open\": \"y\",\n",
        "}\n",
        "\n",
        "df = df.rename(columns={'Date':'ds','Open':'y'})\n",
        "df.columns\n",
        "# TAREA: Utiliza el diccionario para rembrar las columnas de df\n",
        "df\n"
      ],
      "execution_count": null,
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              "            ds     y\n",
              "0   2020-08-01 $0.00\n",
              "1   2020-08-02 $0.00\n",
              "2   2020-08-03 $0.00\n",
              "3   2020-08-04 $0.00\n",
              "4   2020-08-05 $0.00\n",
              "..         ...   ...\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Ejecuta la siguiente línea, si lo has hecho bien hasta aquí no dará error\n",
        "df['ds'] = df['ds'].dt.tz_localize(None)"
      ],
      "metadata": {
        "id": "ERbuEBnPe90l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "rvOredaW3eaD",
        "outputId": "4f374f27-c54b-4135-93ba-4c12cbd940ce"
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            ds     y\n",
              "784 2022-11-04 $0.00\n",
              "785 2022-11-05 $0.00\n",
              "786 2022-11-06 $0.00\n",
              "787 2022-11-07 $0.00\n",
              "788 2022-11-08 $0.00"
            ],
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ds</th>\n",
              "      <th>y</th>\n",
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              "    <tr>\n",
              "      <th>784</th>\n",
              "      <td>2022-11-04</td>\n",
              "      <td>$0.00</td>\n",
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              "    <tr>\n",
              "      <th>785</th>\n",
              "      <td>2022-11-05</td>\n",
              "      <td>$0.00</td>\n",
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              "    <tr>\n",
              "      <th>786</th>\n",
              "      <td>2022-11-06</td>\n",
              "      <td>$0.00</td>\n",
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              "    <tr>\n",
              "      <th>787</th>\n",
              "      <td>2022-11-07</td>\n",
              "      <td>$0.00</td>\n",
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              "      <th>788</th>\n",
              "      <td>2022-11-08</td>\n",
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              "          const element = document.querySelector('#df-b9887451-6669-4f02-908c-3c678d3c1226');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "60SPukrzjLlf",
        "outputId": "a4df3fe8-54bc-42e9-8ab6-bc508c9b6ca6"
      },
      "source": [
        "# Crear la gráfica del precio de apertura\n",
        "#Asigna a la variable x la columna ds\n",
        "x = df[\"ds\"]\n",
        "#Asigna a la variable y la columna y\n",
        "y = df[\"y\"]\n",
        "\n",
        "fig = go.Figure()\n",
        "\n",
        "fig.add_trace(go.Scatter(x=x, y=y))\n",
        "\n",
        "# Le ponemos el título a la gráfica\n",
        "titulo = \"Serie Temporal del Bitcoin\"\n",
        "fig.update_layout(\n",
        "    title_text=titulo,\n",
        ")\n",
        "\n",
        "fig.update_layout(\n",
        "    xaxis=dict(\n",
        "        rangeselector=dict(\n",
        "            buttons=list(\n",
        "                [\n",
        "                    dict(count=1, label=\"1m\", step=\"month\", stepmode=\"backward\"),\n",
        "                    dict(count=6, label=\"6m\", step=\"month\", stepmode=\"backward\"),\n",
        "                    dict(count=1, label=\"YTD\", step=\"year\", stepmode=\"todate\"),\n",
        "                    dict(count=1, label=\"1a\", step=\"year\", stepmode=\"backward\"),\n",
        "                    dict(step=\"all\"),\n",
        "                ]\n",
        "            )\n",
        "        ),\n",
        "        rangeslider=dict(visible=True),\n",
        "        type=\"date\",\n",
        "    )\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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      "cell_type": "markdown",
      "source": [
        "<h3 id=\"version\">Entrenar y predecir el modelo</h3>\n"
      ],
      "metadata": {
        "id": "43Ga6XjFC-ks"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<p>\n",
        "    Ahora que has visto en la gráfica de que datos partimos. Inicializamos el Prophet y lo entramos pasandole el dataframe\n",
        "</p>\n",
        "\n"
      ],
      "metadata": {
        "id": "OrdwuatdC-kt"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NvGVBudz5s3-"
      },
      "source": [
        "#TAREA: Inicializa Prophet en la variable m con seasonality_mode en modo \"multiplicative\"\n",
        "\n",
        "m = Prophet(seasonality_mode= 'multiplicative')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#TAREA: Entrena el modelo m con el dataframe de datos df\n",
        "m.fit(df)"
      ],
      "metadata": {
        "id": "0XVEpKXLhKTh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ddad7310-72eb-48e4-9d54-948b97695ee1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<fbprophet.forecaster.Prophet at 0x7f1f0df7ea10>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<p>\n",
        "    Le decimos al modelo m los días en futuro que queremos predecir, en este caso el próximo año.\n",
        "</p>\n",
        "\n"
      ],
      "metadata": {
        "id": "TJZXEaSkDOC9"
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    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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          "height": 131
        },
        "id": "ZLSmkanEfxK6",
        "outputId": "9d68f7ef-cf41-4cf1-8114-ada94b82d39e"
      },
      "source": [
        "#generar un dataframe con los días que tenemos y los 365 días siguientes a hoy para predecirlo\n",
        "#puedes usar la función del prophet make_future_dataframe()\n",
        "#future = \n",
        "#future.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "SyntaxError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-41-f330ca5740b2>\"\u001b[0;36m, line \u001b[0;32m3\u001b[0m\n\u001b[0;31m    future =\u001b[0m\n\u001b[0m             ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "future = m.make_future_dataframe(periods=365)\n",
        "future.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "c4sjkxgyyFdz",
        "outputId": "06e9cd3c-d3df-4d3d-e72a-4c9e757d2f92"
      },
      "execution_count": null,
      "outputs": [
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          "data": {
            "text/plain": [
              "             ds\n",
              "1149 2023-11-04\n",
              "1150 2023-11-05\n",
              "1151 2023-11-06\n",
              "1152 2023-11-07\n",
              "1153 2023-11-08"
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              "      <td>2023-11-04</td>\n",
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              "      <td>2023-11-05</td>\n",
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              "      <td>2023-11-06</td>\n",
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              "      <td>2023-11-07</td>\n",
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              "      <th>1153</th>\n",
              "      <td>2023-11-08</td>\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4db051ed-f633-442b-b78e-70889d1f2c7c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4db051ed-f633-442b-b78e-70889d1f2c7c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# En el caso que hayas elegido un valor o fondo tradicional, es decir, que no opere los fines\n",
        "# de semana, descomenta la siguiente línea:\n",
        "\n",
        "# future = future[ future['ds'].dt.dayofweek < 5 ] # Nos elimina los fines de semana de 'ds' para no predecirlos\n",
        "future"
      ],
      "metadata": {
        "id": "4XTsqYartl_f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "2e4e0521-cd55-40f5-c035-ffd26bd55bc4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             ds\n",
              "0    2020-08-01\n",
              "1    2020-08-02\n",
              "2    2020-08-03\n",
              "3    2020-08-04\n",
              "4    2020-08-05\n",
              "...         ...\n",
              "1149 2023-11-04\n",
              "1150 2023-11-05\n",
              "1151 2023-11-06\n",
              "1152 2023-11-07\n",
              "1153 2023-11-08\n",
              "\n",
              "[1154 rows x 1 columns]"
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              "\n",
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              "      <td>2020-08-04</td>\n",
              "    </tr>\n",
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              "      <th>4</th>\n",
              "      <td>2020-08-05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>2023-11-06</td>\n",
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              "      <td>2023-11-07</td>\n",
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              "      <td>2023-11-08</td>\n",
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              "<p>1154 rows × 1 columns</p>\n",
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              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-954e7e43-60a7-4ccf-8fde-6deb0f51c913');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<p>\n",
        "    Al modelo entrenado le pasamos el dataframe a predecir.\n",
        "</p>\n",
        "\n"
      ],
      "metadata": {
        "id": "jB2FSrnHDxyj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Utiliza el modelo m para predecir el dataframe future\n",
        "forecast = m.predict(future)"
      ],
      "metadata": {
        "id": "3kAPSoh8ojz-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "QUxvUHl5f0Zt",
        "outputId": "935b4145-9db2-40aa-e851-7c83f8c8b440"
      },
      "source": [
        "forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             ds   yhat  yhat_lower  yhat_upper\n",
              "1149 2023-11-04 $-0.00      $-0.00      $-0.00\n",
              "1150 2023-11-05 $-0.00      $-0.00      $-0.00\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "          const element = document.querySelector('#df-85a7f886-b62e-45e7-9c40-47bf0e469586');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
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              "    </div>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<p>\n",
        "    Gráfica con el modelo entrenado y los valores de un año en futuro predecidos.\n",
        "</p>\n",
        "\n"
      ],
      "metadata": {
        "id": "Uo1nYT1DFUug"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "APomV-ehh8tH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "outputId": "4339f9c0-be4c-4309-e36c-9600308c008f"
      },
      "source": [
        "next_day = (datetime.today() + timedelta(days=1)).strftime('%Y-%m-%d')\n",
        "forecast[forecast['ds'] == next_day]['yhat'].item()\n",
        "plot_plotly(m, forecast)"
      ],
      "execution_count": null,
      "outputs": [
        {
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          "data": {
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