UNIVARIATE_LSTM_TIME_SERIES.ipynb

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      "source": [
        "UNIVARIATE TIME SERIES ANALYSIS USING LSTM"
      ],
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      }
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    {
      "cell_type": "markdown",
      "source": [
        "IMPORTING NECESSARY LIBRARIES"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "IMPORTING THE DATASET"
      ],
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      }
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    {
      "cell_type": "code",
      "source": [
        "dateparse= lambda dates: pd.datetime.strptime(dates,'%d-%m-%Y')\n",
        "data=pd.read_csv('MAster_methanol.csv',parse_dates=['Date'],date_parser=dateparse,usecols=['Date','India Price'])"
      ],
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      "outputs": [
        {
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          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        }
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      "cell_type": "code",
      "source": [
        "#data.dtypes\n",
        "data.index=pd.to_datetime(data['Date'],format='%d-%m-%Y')"
      ],
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        "id": "9LM1O_EInwVv"
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              "      <th>Date</th>\n",
              "      <th>India Price</th>\n",
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              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2005-10-07</th>\n",
              "      <td>2005-10-07</td>\n",
              "      <td>240.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-10-14</th>\n",
              "      <td>2005-10-14</td>\n",
              "      <td>240.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005-10-21</th>\n",
              "      <td>2005-10-21</td>\n",
              "      <td>242.5</td>\n",
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              "      <th>2005-10-28</th>\n",
              "      <td>2005-10-28</td>\n",
              "      <td>242.5</td>\n",
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              "      <td>2005-11-04</td>\n",
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            "text/plain": [
              "                 Date  India Price\n",
              "Date                              \n",
              "2005-10-07 2005-10-07        240.0\n",
              "2005-10-14 2005-10-14        240.0\n",
              "2005-10-21 2005-10-21        242.5\n",
              "2005-10-28 2005-10-28        242.5\n",
              "2005-11-04 2005-11-04        250.0"
            ]
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      "source": [
        "del data['Date']"
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              "      <td>242.5</td>\n",
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              "            India Price\n",
              "Date                   \n",
              "2005-10-07        240.0\n",
              "2005-10-14        240.0\n",
              "2005-10-21        242.5\n",
              "2005-10-28        242.5\n",
              "2005-11-04        250.0"
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          "execution_count": 13
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            "text/plain": [
              "DatetimeIndex(['2005-10-07', '2005-10-14', '2005-10-21', '2005-10-28',\n",
              "               '2005-11-04', '2005-11-11', '2005-11-18', '2005-11-25',\n",
              "               '2005-12-02', '2005-12-09',\n",
              "               ...\n",
              "               '2021-02-19', '2021-02-26', '2021-03-05', '2021-03-12',\n",
              "               '2021-03-19', '2021-03-26', '2021-04-02', '2021-04-09',\n",
              "               '2021-04-16', '2021-04-23'],\n",
              "              dtype='datetime64[ns]', name='Date', length=796, freq=None)"
            ]
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          "execution_count": 14
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "SPLITTING DATA INTO TRAIN AND TEST SET"
      ],
      "metadata": {
        "id": "CbU8P46t_QQS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train=data[:670]"
      ],
      "metadata": {
        "id": "DD_P6Z76oHIn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test=data[670:]"
      ],
      "metadata": {
        "id": "3YweVgS3qHmG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(train),len(test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "POKH-TW_qLz8",
        "outputId": "54385718-3042-4faa-853f-5d6a99eb19ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(670, 126)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "FEATURE SCALING OF DATA"
      ],
      "metadata": {
        "id": "SOoasFV-_W3S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler=MinMaxScaler()\n",
        "# to convert data between scale of 0 to 1"
      ],
      "metadata": {
        "id": "G7CcMpfYqOXB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head(),data.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qclqazVwqQm0",
        "outputId": "d979b883-5ccf-4d1c-84dc-8fa62b5661f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(            India Price\n",
              " Date                   \n",
              " 2005-10-07        240.0\n",
              " 2005-10-14        240.0\n",
              " 2005-10-21        242.5\n",
              " 2005-10-28        242.5\n",
              " 2005-11-04        250.0,             India Price\n",
              " Date                   \n",
              " 2021-03-26        274.0\n",
              " 2021-04-02        274.0\n",
              " 2021-04-09        276.0\n",
              " 2021-04-16        275.0\n",
              " 2021-04-23        275.0)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaler.fit(train) #fit scaler object on training test #cals std ,max,min value"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bIDMqo00qS2J",
        "outputId": "3a26c902-fb23-4a08-c4d1-8301df412c98"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MinMaxScaler()"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#transform the train and test in scalar object values\n",
        "scaled_train=scaler.transform(train)\n",
        "scaled_test=scaler.transform(test)"
      ],
      "metadata": {
        "id": "-gonY5njqVsg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_train[:10] # its an array"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YxKxsQe6qYHJ",
        "outputId": "43e45145-406e-4b44-947a-3e487c67a686"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.19170984],\n",
              "       [0.19170984],\n",
              "       [0.19689119],\n",
              "       [0.19689119],\n",
              "       [0.21243523],\n",
              "       [0.22797927],\n",
              "       [0.23834197],\n",
              "       [0.24352332],\n",
              "       [0.24352332],\n",
              "       [0.24870466]])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_test[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-o4LF5cTqaKi",
        "outputId": "1c5fc095-04a0-4884-f7f6-207208067fbe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.58549223],\n",
              "       [0.50259067],\n",
              "       [0.39896373],\n",
              "       [0.28290155],\n",
              "       [0.24766839],\n",
              "       [0.21243523],\n",
              "       [0.18756477],\n",
              "       [0.17202073],\n",
              "       [0.1761658 ],\n",
              "       [0.17720207]])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "TIME SERIES ANALYSIS USING TENSORFLOW'S TIME SERIES GENERATOR"
      ],
      "metadata": {
        "id": "AzMwiF6N_uJ6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.preprocessing.sequence import TimeseriesGenerator"
      ],
      "metadata": {
        "id": "xqX0fJGoqcex"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#define generator \n",
        "#given data is weekly \n",
        "#3 months=12 weeks data\n",
        "n_input=12\n",
        "n_features=1 #only 1 feature in the data\n",
        "generator=TimeseriesGenerator(scaled_train,scaled_train,length=n_input,batch_size=1)"
      ],
      "metadata": {
        "id": "4xozGnfiqgDH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X,y=generator[0]\n",
        "print(f'Given the array: \\n{X.flatten()}')\n",
        "print(f'Predict the y: \\n {y}')\n",
        "#Given values of 3 months in array ie,12 weeks\n",
        "#predicts 13th week's value"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "phQyHFqgqzv_",
        "outputId": "ca2f877e-748a-469b-b80a-e758b517320d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Given the array: \n",
            "[0.19170984 0.19170984 0.19689119 0.19689119 0.21243523 0.22797927\n",
            " 0.23834197 0.24352332 0.24352332 0.24870466 0.25388601 0.25388601]\n",
            "Predict the y: \n",
            " [[0.26943005]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A PREVIEW OF HOW PREDICTIONS ARE MADE, GIVEN 12 WEEKS DATA ,THE MODEL PREDICTS THE 13th WEEK'S DATA"
      ],
      "metadata": {
        "id": "JDLaabGsAB87"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X,y=generator[1] #take values #from 2nd week and predict the 14th week and so on\n",
        "print(f'Given the array: \\n{X.flatten()}')\n",
        "print(f'Predict the y: \\n {y}')\n",
        "#Given values of 3 months in array ie,12 weeks\n",
        "#predicts 13th week's value"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uRemg7m7rtPV",
        "outputId": "ab3d60be-863c-4c49-b7fc-f07bbd3399a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Given the array: \n",
            "[0.19170984 0.19689119 0.19689119 0.21243523 0.22797927 0.23834197\n",
            " 0.24352332 0.24352332 0.24870466 0.25388601 0.25388601 0.26943005]\n",
            "Predict the y: \n",
            " [[0.26943005]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_T-Ligx_rZ2J",
        "outputId": "e14656b2-817f-474b-e221-1b6c87f8d016"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 12, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "CREATING A LSTM MODEL USING KERAS LIBRARY FROM TENSORFLOW"
      ],
      "metadata": {
        "id": "MV4ef2PWAdVP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import LSTM\n",
        "#calling models\n",
        "#layers added sequential"
      ],
      "metadata": {
        "id": "eEaLDwdSsdgR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#define model\n",
        "model=Sequential()"
      ],
      "metadata": {
        "id": "hhvFGm4As7-F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#LSTM layer with 100 neurons , activation function relu and input shape of data\n",
        "model.add(LSTM(100,activation='relu',input_shape=(n_input,n_features)))\n",
        "model.add(Dense(1)) # model layer final\n",
        "model.compile(optimizer='adam',loss='mse')"
      ],
      "metadata": {
        "id": "-KXWLqY-tCKz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "id": "9hwxqQu1t-wL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9b360812-7ba8-4221-a142-ce7e45181989"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_2 (LSTM)               (None, 100)               40800     \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 101       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 40,901\n",
            "Trainable params: 40,901\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#fit model\n",
        "model.fit(generator,epochs=50)"
      ],
      "metadata": {
        "id": "saYeOIETuTPf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "115df2e8-e65f-4f1c-85f1-6f35f7235163"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "658/658 [==============================] - 5s 6ms/step - loss: 0.0120\n",
            "Epoch 2/50\n",
            "658/658 [==============================] - 5s 8ms/step - loss: 0.0041\n",
            "Epoch 3/50\n",
            "658/658 [==============================] - 4s 5ms/step - loss: 0.0024\n",
            "Epoch 4/50\n",
            "658/658 [==============================] - 4s 6ms/step - loss: 0.0019\n",
            "Epoch 5/50\n",
            "658/658 [==============================] - 4s 5ms/step - loss: 0.0015\n",
            "Epoch 6/50\n",
            "658/658 [==============================] - 4s 6ms/step - loss: 0.0013\n",
            "Epoch 7/50\n",
            "658/658 [==============================] - 4s 5ms/step - loss: 0.0013\n",
            "Epoch 8/50\n",
            "658/658 [==============================] - 4s 5ms/step - loss: 0.0011\n",
            "Epoch 9/50\n",
            "658/658 [==============================] - 4s 5ms/step - loss: 0.0011\n",
            "Epoch 10/50\n",
            "658/658 [==============================] - 4s 5ms/step - loss: 0.0011\n",
            "Epoch 11/50\n",
            "658/658 [==============================] - 3s 5ms/step - loss: 9.6730e-04\n",
            "Epoch 12/50\n",
            "658/658 [==============================] - 4s 6ms/step - loss: 9.4853e-04\n",
            "Epoch 13/50\n",
            "658/658 [==============================] - 3s 5ms/step - loss: 9.5938e-04\n",
            "Epoch 14/50\n",
            "658/658 [==============================] - 4s 5ms/step - loss: 0.0010\n",
            "Epoch 15/50\n",
            "658/658 [==============================] - 3s 5ms/step - loss: 9.8022e-04\n",
            "Epoch 16/50\n",
            "658/658 [==============================] - 4s 6ms/step - loss: 9.9399e-04\n",
            "Epoch 17/50\n",
            "658/658 [==============================] - 4s 6ms/step - loss: 8.9594e-04\n",
            "Epoch 18/50\n",
            "658/658 [==============================] - 4s 5ms/step - loss: 8.7874e-04\n",
            "Epoch 19/50\n",
            "658/658 [==============================] - 4s 6ms/step - loss: 9.3833e-04\n",
            "Epoch 20/50\n",
            "658/658 [==============================] - 4s 6ms/step - loss: 9.0330e-04\n",
            "Epoch 21/50\n",
            "658/658 [==============================] - 4s 5ms/step - loss: 8.4585e-04\n",
            "Epoch 22/50\n",
            "658/658 [==============================] - 4s 6ms/step - loss: 8.6868e-04\n",
            "Epoch 23/50\n",
            "658/658 [==============================] - 4s 5ms/step - loss: 8.7201e-04\n",
            "Epoch 24/50\n",
            "658/658 [==============================] - 4s 5ms/step - loss: 8.5818e-04\n",
            "Epoch 25/50\n",
            "658/658 [==============================] - 3s 5ms/step - loss: 8.4088e-04\n",
            "Epoch 26/50\n",
            "658/658 [==============================] - 3s 5ms/step - loss: 8.8778e-04\n",
            "Epoch 27/50\n",
            "658/658 [==============================] - 4s 5ms/step - loss: 8.1688e-04\n",
            "Epoch 28/50\n",
            "658/658 [==============================] - 4s 5ms/step - loss: 8.4642e-04\n",
            "Epoch 29/50\n",
            "658/658 [==============================] - 4s 5ms/step - loss: 8.8140e-04\n",
            "Epoch 30/50\n",
            "658/658 [==============================] - 4s 5ms/step - loss: 7.9376e-04\n",
            "Epoch 31/50\n",
            "658/658 [==============================] - 4s 6ms/step - loss: 7.8730e-04\n",
            "Epoch 32/50\n",
            "658/658 [==============================] - 5s 8ms/step - loss: 8.6524e-04\n",
            "Epoch 33/50\n",
            "658/658 [==============================] - 4s 5ms/step - loss: 7.8433e-04\n",
            "Epoch 34/50\n",
            "658/658 [==============================] - 4s 5ms/step - loss: 8.4533e-04\n",
            "Epoch 35/50\n",
            "658/658 [==============================] - 3s 5ms/step - loss: 7.9138e-04\n",
            "Epoch 36/50\n",
            "658/658 [==============================] - 3s 5ms/step - loss: 7.9452e-04\n",
            "Epoch 37/50\n",
            "658/658 [==============================] - 4s 5ms/step - loss: 7.9396e-04\n",
            "Epoch 38/50\n",
            "658/658 [==============================] - 4s 5ms/step - loss: 8.0408e-04\n",
            "Epoch 39/50\n",
            "658/658 [==============================] - 4s 6ms/step - loss: 8.0631e-04\n",
            "Epoch 40/50\n",
            "658/658 [==============================] - 4s 6ms/step - loss: 8.2751e-04\n",
            "Epoch 41/50\n",
            "658/658 [==============================] - 4s 5ms/step - loss: 8.2005e-04\n",
            "Epoch 42/50\n",
            "658/658 [==============================] - 4s 5ms/step - loss: 8.0335e-04\n",
            "Epoch 43/50\n",
            "658/658 [==============================] - 4s 6ms/step - loss: 8.0544e-04\n",
            "Epoch 44/50\n",
            "658/658 [==============================] - 4s 6ms/step - loss: 8.3699e-04\n",
            "Epoch 45/50\n",
            "658/658 [==============================] - 3s 5ms/step - loss: 7.5960e-04\n",
            "Epoch 46/50\n",
            "658/658 [==============================] - 4s 5ms/step - loss: 7.9664e-04\n",
            "Epoch 47/50\n",
            "658/658 [==============================] - 4s 5ms/step - loss: 8.4443e-04\n",
            "Epoch 48/50\n",
            "658/658 [==============================] - 3s 5ms/step - loss: 7.6703e-04\n",
            "Epoch 49/50\n",
            "658/658 [==============================] - 3s 5ms/step - loss: 8.0431e-04\n",
            "Epoch 50/50\n",
            "658/658 [==============================] - 4s 5ms/step - loss: 8.0797e-04\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fe49bf21b90>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss_per_epoch=model.history.history['loss']\n",
        "plt.plot(range(len(loss_per_epoch)),loss_per_epoch)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "kkXpN5EgucBO",
        "outputId": "b8cb764d-0b49-4bba-94a6-b90a4af517d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fe4993fed90>]"
            ]
          },
          "metadata": {},
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "AS EPOCHS BECAME CONSTANT AROUND 15,I COULD HAVE STOPPED MY EPOCHS AT 15."
      ],
      "metadata": {
        "id": "MF9HfCl_B0Mb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "FUTURE PREDICTION- FORECASTING VALUES"
      ],
      "metadata": {
        "id": "lSvXFOcjCFkq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A PREVIEW OF HOW VALUES WILL BE PREDICTED"
      ],
      "metadata": {
        "id": "0l1JjyDXCMF0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "last_train_batch= scaled_train[-12:]# last 3 months data of training test to predict the first value of test set"
      ],
      "metadata": {
        "id": "1P9q_lrxw9rX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "last_train_batch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KjYf_KAs67ay",
        "outputId": "5412ec15-1dd4-45cf-c3f3-d141d6427795"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.47150259],\n",
              "       [0.46735751],\n",
              "       [0.4611399 ],\n",
              "       [0.4611399 ],\n",
              "       [0.45906736],\n",
              "       [0.45906736],\n",
              "       [0.4507772 ],\n",
              "       [0.4611399 ],\n",
              "       [0.47150259],\n",
              "       [0.61761658],\n",
              "       [0.63834197],\n",
              "       [0.62487047]])"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "last_train_batch=last_train_batch.reshape((1,n_input,n_features))"
      ],
      "metadata": {
        "id": "H1ZvBYEgU8bg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict(last_train_batch)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-6P3CumRh51q",
        "outputId": "a6706287-55ee-475f-c74b-ae5d243aedeb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.6261526]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_test[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ivFkA10_iETK",
        "outputId": "53308346-a532-4097-d454-6a4e073415ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.58549223])"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see the predicted value (0.62) and actual scaled test value(0.58) are close , so the model is predicting good."
      ],
      "metadata": {
        "id": "LYw0-mkpCX1O"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "MAKING THE ACTUAL FUTURE PREDICTIONS"
      ],
      "metadata": {
        "id": "7k49aqPfCnr7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_predictions=[]\n",
        "\n",
        "first_batch=scaled_train[-n_input:]\n",
        "curr_batch=first_batch.reshape((1,n_input,n_features))\n",
        "\n",
        "for i in range(len(test)):\n",
        "  #prediction value for first batch\n",
        "  curr_pred=model.predict(curr_batch)[0]\n",
        "  #append prediction into array\n",
        "  test_predictions.append(curr_pred)\n",
        "  curr_batch=np.append(curr_batch[:,1:,:],[[curr_pred]],axis=1)"
      ],
      "metadata": {
        "id": "AFhnaQMDiF6L"
      },
      "execution_count": null,
      "outputs": []
    },
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      "source": [
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      "metadata": {
        "id": "m0CBtsijHi3K",
        "outputId": "a3c3a48a-f223-41cf-c639-f9edb1bcab12",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
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          "execution_count": 61
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        "id": "UcVJcAg95LZR",
        "outputId": "1cc8123b-6e10-4665-ff3c-0a3087f47bcc"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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            "text/plain": [
              "(1, 12, 1)"
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            ],
            "text/plain": [
              "            India Price\n",
              "Date                   \n",
              "2018-11-02        430.0\n",
              "2018-11-09        390.0\n",
              "2018-11-16        340.0\n",
              "2018-11-23        284.0\n",
              "2018-11-30        267.0"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "SCALING VALUES BACK TO ORIGINAL UNITS"
      ],
      "metadata": {
        "id": "atWDPoDeC0vk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "true_predictions=scaler.inverse_transform(test_predictions)"
      ],
      "metadata": {
        "id": "AwsK_PsRvNa7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test['Predictions']=true_predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VgG_jQgCxEYx",
        "outputId": "ce97af72-d10f-4677-f47f-f8f79f89dc3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ACTUAL VALUES VS PREDICTED VALUES OF INDIA PRICE"
      ],
      "metadata": {
        "id": "uYE7JDQhDFRB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "tSCz99RvxVgt",
        "outputId": "d7c509a5-5dc3-4752-c510-d03009e2c0f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>India Price</th>\n",
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              "      <th>2018-11-30</th>\n",
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              "                                                     [key], {});\n",
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            "text/plain": [
              "            India Price  Predictions\n",
              "Date                                \n",
              "2018-11-02        430.0   449.618617\n",
              "2018-11-09        390.0   443.813392\n",
              "2018-11-16        340.0   440.199074\n",
              "2018-11-23        284.0   435.602830\n",
              "2018-11-30        267.0   430.442214"
            ]
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          "execution_count": 75
        }
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    {
      "cell_type": "code",
      "source": [
        "test.plot()"
      ],
      "metadata": {
        "colab": {
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          "height": 294
        },
        "id": "MsXEy7hHxhhf",
        "outputId": "bd3b6b18-4896-4fda-9ef1-ac3ae03fb913"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe49bdc9410>"
            ]
          },
          "metadata": {},
          "execution_count": 76
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "CALCULATING ROOT MEAN SQUARE ERROR"
      ],
      "metadata": {
        "id": "NVYdGfyaC8xu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "from math import sqrt"
      ],
      "metadata": {
        "id": "inYH0dbrxq7w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "error=sqrt(mean_squared_error(test['India Price'],test['Predictions']))"
      ],
      "metadata": {
        "id": "oXOf2lqlyND5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(error)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K0fh_ovpylyC",
        "outputId": "9348c766-aa5e-4dff-9c0e-9242b5a5eb56"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "175.50902517918877\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "Zw6_z8HnDOJi"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}