Bharti_Airtel_Stock_Prediction.ipynb

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        "#RNN Model\n"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import yfinance as yf"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('Airtel.csv', index_col=['Date'], parse_dates=True)\n",
        "df.head()\n"
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              "              OPEN   HIGH    LOW  PREV. CLOSE    ltp   close    vwap  52W H  \\\n",
              "Date                                                                          \n",
              "2021-06-02  529.90  534.6  528.0       532.90  529.5  529.50  530.22  623.0   \n",
              "2021-06-03  530.00  533.4  528.5       529.50  531.2  532.05  531.21  623.0   \n",
              "2021-06-04  533.95  535.8  531.2       532.05  532.6  532.70  533.34  623.0   \n",
              "2021-06-07  534.00  541.0  533.0       532.70  536.3  536.45  537.66  623.0   \n",
              "2021-06-08  538.10  548.0  534.0       536.45  547.5  546.80  541.30  623.0   \n",
              "\n",
              "            52W L      VOLUME             VALUE No of trades  \n",
              "Date                                                          \n",
              "2021-06-02  394.0  10,372,845  5,499,910,242.45      111,242  \n",
              "2021-06-03  394.0   6,174,103  3,279,761,456.40      112,639  \n",
              "2021-06-04  394.0   4,788,898  2,554,100,070.05       86,667  \n",
              "2021-06-07  394.0   9,496,183  5,105,741,297.30      125,647  \n",
              "2021-06-08  394.0  12,586,649  6,813,116,864.75      141,711  "
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              "      <th>2021-06-02</th>\n",
              "      <td>529.90</td>\n",
              "      <td>534.6</td>\n",
              "      <td>528.0</td>\n",
              "      <td>532.90</td>\n",
              "      <td>529.5</td>\n",
              "      <td>529.50</td>\n",
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              "      <th>2021-06-03</th>\n",
              "      <td>530.00</td>\n",
              "      <td>533.4</td>\n",
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              "      <td>394.0</td>\n",
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              "      <th>2021-06-04</th>\n",
              "      <td>533.95</td>\n",
              "      <td>535.8</td>\n",
              "      <td>531.2</td>\n",
              "      <td>532.05</td>\n",
              "      <td>532.6</td>\n",
              "      <td>532.70</td>\n",
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              "      <td>623.0</td>\n",
              "      <td>394.0</td>\n",
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              "      <td>2,554,100,070.05</td>\n",
              "      <td>86,667</td>\n",
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              "      <th>2021-06-07</th>\n",
              "      <td>534.00</td>\n",
              "      <td>541.0</td>\n",
              "      <td>533.0</td>\n",
              "      <td>532.70</td>\n",
              "      <td>536.3</td>\n",
              "      <td>536.45</td>\n",
              "      <td>537.66</td>\n",
              "      <td>623.0</td>\n",
              "      <td>394.0</td>\n",
              "      <td>9,496,183</td>\n",
              "      <td>5,105,741,297.30</td>\n",
              "      <td>125,647</td>\n",
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              "      <th>2021-06-08</th>\n",
              "      <td>538.10</td>\n",
              "      <td>548.0</td>\n",
              "      <td>534.0</td>\n",
              "      <td>536.45</td>\n",
              "      <td>547.5</td>\n",
              "      <td>546.80</td>\n",
              "      <td>541.30</td>\n",
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              "      <td>394.0</td>\n",
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 497 entries, 2021-06-02 to 2023-06-01\n",
            "Data columns (total 12 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   OPEN          497 non-null    float64\n",
            " 1   HIGH          497 non-null    float64\n",
            " 2   LOW           497 non-null    float64\n",
            " 3   PREV. CLOSE   497 non-null    float64\n",
            " 4   ltp           497 non-null    float64\n",
            " 5   close         497 non-null    float64\n",
            " 6   vwap          497 non-null    float64\n",
            " 7   52W H         497 non-null    float64\n",
            " 8   52W L         497 non-null    float64\n",
            " 9   VOLUME        497 non-null    object \n",
            " 10  VALUE         497 non-null    object \n",
            " 11  No of trades  497 non-null    object \n",
            "dtypes: float64(9), object(3)\n",
            "memory usage: 50.5+ KB\n"
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bkeRIGeQ04wb",
        "outputId": "26a00c79-381a-45dc-9275-572048b3c074"
      },
      "execution_count": 242,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "497"
            ]
          },
          "metadata": {},
          "execution_count": 242
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "airtel_close = df['close']"
      ],
      "metadata": {
        "id": "_GFZd4J81DOg"
      },
      "execution_count": 243,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "airtel_close.reset_index(drop=True, inplace=True)\n",
        "airtel_close.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MiEEdq2r6g2E",
        "outputId": "8b979809-25d8-411c-e523-1135bbf53924"
      },
      "execution_count": 244,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(497,)"
            ]
          },
          "metadata": {},
          "execution_count": 244
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "airtel_close = pd.DataFrame(airtel_close)"
      ],
      "metadata": {
        "id": "LIsUTaJz61dr"
      },
      "execution_count": 245,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train = airtel_close.iloc[:480]\n",
        "test = airtel_close.iloc[480:]"
      ],
      "metadata": {
        "id": "yydkoxmz6_9o"
      },
      "execution_count": 246,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "metadata": {
        "id": "Iq4b13V_7Scn"
      },
      "execution_count": 247,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = MinMaxScaler()"
      ],
      "metadata": {
        "id": "bfZRm8Tt7ZXb"
      },
      "execution_count": 248,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler.fit(train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "lH2z32K37hS9",
        "outputId": "5247e872-813c-452b-83da-9f24b9577be4"
      },
      "execution_count": 249,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MinMaxScaler()"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MinMaxScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 249
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_train = scaler.transform(train)\n",
        "scaled_test = scaler.transform(test)"
      ],
      "metadata": {
        "id": "XuWvSyBj7m7r"
      },
      "execution_count": 250,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Creating the Model"
      ],
      "metadata": {
        "id": "t5rmOkF18H4U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator"
      ],
      "metadata": {
        "id": "sPpNECqY725V"
      },
      "execution_count": 251,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HHA3ujaX8DDO",
        "outputId": "a671e88f-c6dd-44c8-d707-d6e86806432c"
      },
      "execution_count": 252,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(17, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 252
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "help(TimeseriesGenerator)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YSkhY73v8RLq",
        "outputId": "4df286d8-78e6-4237-8663-1023b4326d47"
      },
      "execution_count": 253,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Help on class TimeseriesGenerator in module keras.preprocessing.sequence:\n",
            "\n",
            "class TimeseriesGenerator(keras.utils.data_utils.Sequence)\n",
            " |  TimeseriesGenerator(data, targets, length, sampling_rate=1, stride=1, start_index=0, end_index=None, shuffle=False, reverse=False, batch_size=128)\n",
            " |  \n",
            " |  Utility class for generating batches of temporal data.\n",
            " |  \n",
            " |  Deprecated: `tf.keras.preprocessing.sequence.TimeseriesGenerator` does not\n",
            " |  operate on tensors and is not recommended for new code. Prefer using a\n",
            " |  `tf.data.Dataset` which provides a more efficient and flexible mechanism for\n",
            " |  batching, shuffling, and windowing input. See the\n",
            " |  [tf.data guide](https://www.tensorflow.org/guide/data) for more details.\n",
            " |  \n",
            " |  This class takes in a sequence of data-points gathered at\n",
            " |  equal intervals, along with time series parameters such as\n",
            " |  stride, length of history, etc., to produce batches for\n",
            " |  training/validation.\n",
            " |  \n",
            " |  Arguments:\n",
            " |      data: Indexable generator (such as list or Numpy array)\n",
            " |          containing consecutive data points (timesteps).\n",
            " |          The data should be at 2D, and axis 0 is expected\n",
            " |          to be the time dimension.\n",
            " |      targets: Targets corresponding to timesteps in `data`.\n",
            " |          It should have same length as `data`.\n",
            " |      length: Length of the output sequences (in number of timesteps).\n",
            " |      sampling_rate: Period between successive individual timesteps\n",
            " |          within sequences. For rate `r`, timesteps\n",
            " |          `data[i]`, `data[i-r]`, ... `data[i - length]`\n",
            " |          are used for create a sample sequence.\n",
            " |      stride: Period between successive output sequences.\n",
            " |          For stride `s`, consecutive output samples would\n",
            " |          be centered around `data[i]`, `data[i+s]`, `data[i+2*s]`, etc.\n",
            " |      start_index: Data points earlier than `start_index` will not be used\n",
            " |          in the output sequences. This is useful to reserve part of the\n",
            " |          data for test or validation.\n",
            " |      end_index: Data points later than `end_index` will not be used\n",
            " |          in the output sequences. This is useful to reserve part of the\n",
            " |          data for test or validation.\n",
            " |      shuffle: Whether to shuffle output samples,\n",
            " |          or instead draw them in chronological order.\n",
            " |      reverse: Boolean: if `true`, timesteps in each output sample will be\n",
            " |          in reverse chronological order.\n",
            " |      batch_size: Number of timeseries samples in each batch\n",
            " |          (except maybe the last one).\n",
            " |  \n",
            " |  Returns:\n",
            " |      A [Sequence](\n",
            " |      https://www.tensorflow.org/api_docs/python/tf/keras/utils/Sequence)\n",
            " |      instance.\n",
            " |  \n",
            " |  Examples:\n",
            " |      ```python\n",
            " |      from keras.preprocessing.sequence import TimeseriesGenerator\n",
            " |      import numpy as np\n",
            " |      data = np.array([[i] for i in range(50)])\n",
            " |      targets = np.array([[i] for i in range(50)])\n",
            " |      data_gen = TimeseriesGenerator(data, targets,\n",
            " |                                     length=10, sampling_rate=2,\n",
            " |                                     batch_size=2)\n",
            " |      assert len(data_gen) == 20\n",
            " |      batch_0 = data_gen[0]\n",
            " |      x, y = batch_0\n",
            " |      assert np.array_equal(x,\n",
            " |                            np.array([[[0], [2], [4], [6], [8]],\n",
            " |                                      [[1], [3], [5], [7], [9]]]))\n",
            " |      assert np.array_equal(y,\n",
            " |                            np.array([[10], [11]]))\n",
            " |      ```\n",
            " |  \n",
            " |  Method resolution order:\n",
            " |      TimeseriesGenerator\n",
            " |      keras.utils.data_utils.Sequence\n",
            " |      builtins.object\n",
            " |  \n",
            " |  Methods defined here:\n",
            " |  \n",
            " |  __getitem__(self, index)\n",
            " |      Gets batch at position `index`.\n",
            " |      \n",
            " |      Args:\n",
            " |          index: position of the batch in the Sequence.\n",
            " |      \n",
            " |      Returns:\n",
            " |          A batch\n",
            " |  \n",
            " |  __init__(self, data, targets, length, sampling_rate=1, stride=1, start_index=0, end_index=None, shuffle=False, reverse=False, batch_size=128)\n",
            " |      Initialize self.  See help(type(self)) for accurate signature.\n",
            " |  \n",
            " |  __len__(self)\n",
            " |      Number of batch in the Sequence.\n",
            " |      \n",
            " |      Returns:\n",
            " |          The number of batches in the Sequence.\n",
            " |  \n",
            " |  get_config(self)\n",
            " |      Returns the TimeseriesGenerator configuration as Python dictionary.\n",
            " |      \n",
            " |      Returns:\n",
            " |          A Python dictionary with the TimeseriesGenerator configuration.\n",
            " |  \n",
            " |  to_json(self, **kwargs)\n",
            " |      Returns a JSON string containing the generator's configuration.\n",
            " |      \n",
            " |      Args:\n",
            " |          **kwargs: Additional keyword arguments to be passed\n",
            " |              to `json.dumps()`.\n",
            " |      \n",
            " |      Returns:\n",
            " |          A JSON string containing the tokenizer configuration.\n",
            " |  \n",
            " |  ----------------------------------------------------------------------\n",
            " |  Methods inherited from keras.utils.data_utils.Sequence:\n",
            " |  \n",
            " |  __iter__(self)\n",
            " |      Create a generator that iterate over the Sequence.\n",
            " |  \n",
            " |  on_epoch_end(self)\n",
            " |      Method called at the end of every epoch.\n",
            " |  \n",
            " |  ----------------------------------------------------------------------\n",
            " |  Data descriptors inherited from keras.utils.data_utils.Sequence:\n",
            " |  \n",
            " |  __dict__\n",
            " |      dictionary for instance variables (if defined)\n",
            " |  \n",
            " |  __weakref__\n",
            " |      list of weak references to the object (if defined)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "length = 17\n",
        "batch_size = 30\n",
        "generator = TimeseriesGenerator(scaled_train, scaled_train, length = length, batch_size = batch_size)"
      ],
      "metadata": {
        "id": "NARRlrcP-lWj"
      },
      "execution_count": 254,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, SimpleRNN, LSTM, Dropout"
      ],
      "metadata": {
        "id": "akTXa-B6_T5a"
      },
      "execution_count": 255,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "n_features = 1"
      ],
      "metadata": {
        "id": "BiwjDgwa_p7z"
      },
      "execution_count": 256,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(SimpleRNN(30,input_shape=(length, n_features)))\n",
        "model.add(Dropout(rate = 0.2))\n",
        "# model.add(Dense(17))\n",
        "# model.add(Dense(50))\n",
        "# model.add(Dense(29))\n",
        "model.add(Dense(1))\n",
        "model.compile(optimizer='adam', loss='mse')"
      ],
      "metadata": {
        "id": "CmjoM72X_vv6"
      },
      "execution_count": 257,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZdYTJT0-AbcP",
        "outputId": "bacaabf2-8231-4359-a9ce-888e554cdb3d"
      },
      "execution_count": 258,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_16\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " simple_rnn_16 (SimpleRNN)   (None, 30)                960       \n",
            "                                                                 \n",
            " dropout_20 (Dropout)        (None, 30)                0         \n",
            "                                                                 \n",
            " dense_46 (Dense)            (None, 1)                 31        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 991\n",
            "Trainable params: 991\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit_generator(generator,epochs=30,shuffle=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O7u1LDnlAidK",
        "outputId": "850ba2b8-16a4-4405-ef38-97bca8c19fb9"
      },
      "execution_count": 259,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-259-4a1e14edf27a>:1: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  model.fit_generator(generator,epochs=30,shuffle=False)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "16/16 [==============================] - 3s 21ms/step - loss: 0.0516\n",
            "Epoch 2/30\n",
            "16/16 [==============================] - 0s 15ms/step - loss: 0.0210\n",
            "Epoch 3/30\n",
            "16/16 [==============================] - 0s 17ms/step - loss: 0.0168\n",
            "Epoch 4/30\n",
            "16/16 [==============================] - 0s 16ms/step - loss: 0.0121\n",
            "Epoch 5/30\n",
            "16/16 [==============================] - 0s 15ms/step - loss: 0.0105\n",
            "Epoch 6/30\n",
            "16/16 [==============================] - 0s 16ms/step - loss: 0.0103\n",
            "Epoch 7/30\n",
            "16/16 [==============================] - 0s 16ms/step - loss: 0.0092\n",
            "Epoch 8/30\n",
            "16/16 [==============================] - 0s 14ms/step - loss: 0.0080\n",
            "Epoch 9/30\n",
            "16/16 [==============================] - 0s 16ms/step - loss: 0.0079\n",
            "Epoch 10/30\n",
            "16/16 [==============================] - 0s 15ms/step - loss: 0.0067\n",
            "Epoch 11/30\n",
            "16/16 [==============================] - 0s 17ms/step - loss: 0.0070\n",
            "Epoch 12/30\n",
            "16/16 [==============================] - 0s 16ms/step - loss: 0.0071\n",
            "Epoch 13/30\n",
            "16/16 [==============================] - 0s 18ms/step - loss: 0.0059\n",
            "Epoch 14/30\n",
            "16/16 [==============================] - 0s 15ms/step - loss: 0.0068\n",
            "Epoch 15/30\n",
            "16/16 [==============================] - 0s 18ms/step - loss: 0.0059\n",
            "Epoch 16/30\n",
            "16/16 [==============================] - 0s 17ms/step - loss: 0.0071\n",
            "Epoch 17/30\n",
            "16/16 [==============================] - 0s 16ms/step - loss: 0.0071\n",
            "Epoch 18/30\n",
            "16/16 [==============================] - 0s 17ms/step - loss: 0.0057\n",
            "Epoch 19/30\n",
            "16/16 [==============================] - 0s 15ms/step - loss: 0.0058\n",
            "Epoch 20/30\n",
            "16/16 [==============================] - 0s 14ms/step - loss: 0.0058\n",
            "Epoch 21/30\n",
            "16/16 [==============================] - 0s 17ms/step - loss: 0.0062\n",
            "Epoch 22/30\n",
            "16/16 [==============================] - 0s 18ms/step - loss: 0.0048\n",
            "Epoch 23/30\n",
            "16/16 [==============================] - 0s 17ms/step - loss: 0.0052\n",
            "Epoch 24/30\n",
            "16/16 [==============================] - 0s 15ms/step - loss: 0.0058\n",
            "Epoch 25/30\n",
            "16/16 [==============================] - 0s 14ms/step - loss: 0.0059\n",
            "Epoch 26/30\n",
            "16/16 [==============================] - 0s 15ms/step - loss: 0.0057\n",
            "Epoch 27/30\n",
            "16/16 [==============================] - 0s 14ms/step - loss: 0.0057\n",
            "Epoch 28/30\n",
            "16/16 [==============================] - 0s 15ms/step - loss: 0.0057\n",
            "Epoch 29/30\n",
            "16/16 [==============================] - 0s 14ms/step - loss: 0.0049\n",
            "Epoch 30/30\n",
            "16/16 [==============================] - 0s 16ms/step - loss: 0.0045\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fb9d06c41c0>"
            ]
          },
          "metadata": {},
          "execution_count": 259
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "losses = pd.DataFrame(model.history.history)"
      ],
      "metadata": {
        "id": "zVNbKWyoBcNh"
      },
      "execution_count": 260,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "losses.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "95qF9eYKCoJ7",
        "outputId": "adcc8220-48a0-4269-e572-19c15976b2c6"
      },
      "execution_count": 261,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 261
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "first_eval_batch = scaled_train[-length:]"
      ],
      "metadata": {
        "id": "fU1esGLeCpjg"
      },
      "execution_count": 262,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "first_eval_batch = first_eval_batch.reshape((1,length,1))"
      ],
      "metadata": {
        "id": "mrWiGgAKmkak"
      },
      "execution_count": 263,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "first_eval_batch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BPX6s3bUnAAc",
        "outputId": "443c2570-b7cc-4d92-c0dd-90b7c7194e84"
      },
      "execution_count": 264,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[0.7438746 ],\n",
              "        [0.72135139],\n",
              "        [0.72165576],\n",
              "        [0.73656978],\n",
              "        [0.7598539 ],\n",
              "        [0.7382438 ],\n",
              "        [0.7231776 ],\n",
              "        [0.76076701],\n",
              "        [0.76335413],\n",
              "        [0.80307411],\n",
              "        [0.84203318],\n",
              "        [0.81433572],\n",
              "        [0.78115964],\n",
              "        [0.81098767],\n",
              "        [0.80550905],\n",
              "        [0.82514077],\n",
              "        [0.82468422]]])"
            ]
          },
          "metadata": {},
          "execution_count": 264
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict(first_eval_batch)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G-Y0Wg0Jnb-K",
        "outputId": "6adc6825-4957-47b6-eb06-e553fd6b7eda"
      },
      "execution_count": 265,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 141ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.8245517]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 265
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_test[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "28oZbrM0ng2G",
        "outputId": "c9d6e75d-3ac7-4591-ff42-713617399fdc"
      },
      "execution_count": 266,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.83214123])"
            ]
          },
          "metadata": {},
          "execution_count": 266
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_predictions = []\n",
        "first_eval_batch = scaled_train[-length:]\n",
        "current_batch = first_eval_batch.reshape((1,length,n_features))\n",
        "\n",
        "for i in range(len(test)):\n",
        "  current_pred = model.predict(current_batch)[0]\n",
        "\n",
        "  test_predictions.append(current_pred)\n",
        "\n",
        "  current_batch = np.append(current_batch[:,1:,:],[[current_pred]],axis=1)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UP7w25S6nnSJ",
        "outputId": "d5610e48-827f-4a22-ab42-766fb3f91071"
      },
      "execution_count": 267,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 53ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 40ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 39ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1QSwT-DWpA7g",
        "outputId": "691eb9d0-2068-49b0-96e2-d2d932bb050e"
      },
      "execution_count": 268,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([0.8245517], dtype=float32),\n",
              " array([0.8288008], dtype=float32),\n",
              " array([0.82824486], dtype=float32),\n",
              " array([0.8389254], dtype=float32),\n",
              " array([0.84405774], dtype=float32),\n",
              " array([0.84568423], dtype=float32),\n",
              " array([0.8424325], dtype=float32),\n",
              " array([0.84685475], dtype=float32),\n",
              " array([0.85118294], dtype=float32),\n",
              " array([0.85085166], dtype=float32),\n",
              " array([0.85675055], dtype=float32),\n",
              " array([0.86472565], dtype=float32),\n",
              " array([0.86178297], dtype=float32),\n",
              " array([0.8665064], dtype=float32),\n",
              " array([0.86954445], dtype=float32),\n",
              " array([0.8726818], dtype=float32),\n",
              " array([0.87640226], dtype=float32)]"
            ]
          },
          "metadata": {},
          "execution_count": 268
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "true_predictions = scaler.inverse_transform(test_predictions)"
      ],
      "metadata": {
        "id": "x1UxcbhRpEnC"
      },
      "execution_count": 269,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "true_predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aqHDVuJCpWJZ",
        "outputId": "d51416d7-5247-4504-c57e-067a0670951c"
      },
      "execution_count": 270,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[793.55646154],\n",
              "       [794.95250201],\n",
              "       [794.76985038],\n",
              "       [798.27894715],\n",
              "       [799.96517008],\n",
              "       [800.49955387],\n",
              "       [799.43119752],\n",
              "       [800.88412691],\n",
              "       [802.30615416],\n",
              "       [802.19731125],\n",
              "       [804.13539251],\n",
              "       [806.7556121 ],\n",
              "       [805.78879415],\n",
              "       [807.34067697],\n",
              "       [808.33882789],\n",
              "       [809.36960426],\n",
              "       [810.59196215]])"
            ]
          },
          "metadata": {},
          "execution_count": 270
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test['predictions'] = true_predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ZVtk6-mpYIP",
        "outputId": "67197580-7405-4ab1-8f6e-497fe7dc038a"
      },
      "execution_count": 271,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-271-fa6c3af7c69f>: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",
            "  test['predictions'] = true_predictions\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "SVijjH1Hp0qj",
        "outputId": "9ddcdf97-6ef6-4abc-a908-2c31e0abafee"
      },
      "execution_count": 272,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      close  predictions\n",
              "480  796.05   793.556462\n",
              "481  788.60   794.952502\n",
              "482  791.95   794.769850\n",
              "483  797.05   798.278947\n",
              "484  787.35   799.965170\n",
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              "                                                     [key], {});\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 272
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test.plot(figsize=(12,8))\n",
        "airtel_close.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "TZ7hphHbp5F3",
        "outputId": "318171d9-97ef-498d-858e-e1522a3ed1dc"
      },
      "execution_count": 273,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 273
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Now building model on full dataset\n"
      ],
      "metadata": {
        "id": "2lU2a8p9rIOb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "full_scaler = MinMaxScaler()\n",
        "scaled_full_data = full_scaler.fit_transform(airtel_close)"
      ],
      "metadata": {
        "id": "x8SNTfQDqJa9"
      },
      "execution_count": 274,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generator = TimeseriesGenerator(scaled_full_data,scaled_full_data,length = length,batch_size = 32)"
      ],
      "metadata": {
        "id": "dScVM55vrg5U"
      },
      "execution_count": 275,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "full_model = Sequential()\n",
        "full_model.add(SimpleRNN(30,input_shape=(length,n_features)))\n",
        "full_model.add(Dropout(rate = 0.2))\n",
        "# full_model.add(Dense(170))\n",
        "# full_model.add(Dropout(rate = 0.6))\n",
        "# full_model.add(Dense(500))\n",
        "# full_model.add(Dropout(rate = 0.6))\n",
        "# full_model.add(Dense(500))\n",
        "# full_model.add(Dropout(rate = 0.398))\n",
        "# full_model.add(Dense(500))\n",
        "# full_model.add(Dropout(rate = 0.3))\n",
        "# full_model.add(Dense(500))\n",
        "# full_model.add(Dropout(rate = 0.4))\n",
        "# full_model.add(Dense(290))\n",
        "# full_model.add(Dropout(rate = 0.29))\n",
        "# full_model.add(Dense(229))\n",
        "full_model.add(Dense(1))\n",
        "full_model.compile(optimizer='adam', loss='mse')"
      ],
      "metadata": {
        "id": "hj7h4NlBrwVy"
      },
      "execution_count": 276,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "full_model.fit_generator(generator,epochs=30,shuffle=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7tM1MW7msLxa",
        "outputId": "395b4ebb-1dac-4949-d7ff-7e7bb7b32d5f"
      },
      "execution_count": 277,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-277-e668ab5dba9a>:1: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  full_model.fit_generator(generator,epochs=30,shuffle=False)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "15/15 [==============================] - 4s 18ms/step - loss: 0.7991\n",
            "Epoch 2/30\n",
            "15/15 [==============================] - 0s 19ms/step - loss: 0.2564\n",
            "Epoch 3/30\n",
            "15/15 [==============================] - 0s 20ms/step - loss: 0.0997\n",
            "Epoch 4/30\n",
            "15/15 [==============================] - 0s 19ms/step - loss: 0.0669\n",
            "Epoch 5/30\n",
            "15/15 [==============================] - 0s 19ms/step - loss: 0.0597\n",
            "Epoch 6/30\n",
            "15/15 [==============================] - 0s 18ms/step - loss: 0.0515\n",
            "Epoch 7/30\n",
            "15/15 [==============================] - 0s 18ms/step - loss: 0.0492\n",
            "Epoch 8/30\n",
            "15/15 [==============================] - 0s 20ms/step - loss: 0.0331\n",
            "Epoch 9/30\n",
            "15/15 [==============================] - 0s 18ms/step - loss: 0.0222\n",
            "Epoch 10/30\n",
            "15/15 [==============================] - 0s 18ms/step - loss: 0.0204\n",
            "Epoch 11/30\n",
            "15/15 [==============================] - 0s 19ms/step - loss: 0.0203\n",
            "Epoch 12/30\n",
            "15/15 [==============================] - 0s 19ms/step - loss: 0.0224\n",
            "Epoch 13/30\n",
            "15/15 [==============================] - 0s 17ms/step - loss: 0.0155\n",
            "Epoch 14/30\n",
            "15/15 [==============================] - 0s 19ms/step - loss: 0.0219\n",
            "Epoch 15/30\n",
            "15/15 [==============================] - 0s 19ms/step - loss: 0.0214\n",
            "Epoch 16/30\n",
            "15/15 [==============================] - 0s 18ms/step - loss: 0.0170\n",
            "Epoch 17/30\n",
            "15/15 [==============================] - 0s 18ms/step - loss: 0.0155\n",
            "Epoch 18/30\n",
            "15/15 [==============================] - 0s 17ms/step - loss: 0.0152\n",
            "Epoch 19/30\n",
            "15/15 [==============================] - 0s 19ms/step - loss: 0.0143\n",
            "Epoch 20/30\n",
            "15/15 [==============================] - 0s 18ms/step - loss: 0.0145\n",
            "Epoch 21/30\n",
            "15/15 [==============================] - 0s 29ms/step - loss: 0.0138\n",
            "Epoch 22/30\n",
            "15/15 [==============================] - 0s 31ms/step - loss: 0.0133\n",
            "Epoch 23/30\n",
            "15/15 [==============================] - 0s 32ms/step - loss: 0.0144\n",
            "Epoch 24/30\n",
            "15/15 [==============================] - 1s 33ms/step - loss: 0.0139\n",
            "Epoch 25/30\n",
            "15/15 [==============================] - 0s 32ms/step - loss: 0.0100\n",
            "Epoch 26/30\n",
            "15/15 [==============================] - 0s 18ms/step - loss: 0.0142\n",
            "Epoch 27/30\n",
            "15/15 [==============================] - 0s 19ms/step - loss: 0.0135\n",
            "Epoch 28/30\n",
            "15/15 [==============================] - 0s 16ms/step - loss: 0.0127\n",
            "Epoch 29/30\n",
            "15/15 [==============================] - 0s 17ms/step - loss: 0.0117\n",
            "Epoch 30/30\n",
            "15/15 [==============================] - 0s 19ms/step - loss: 0.0110\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fb9d02ed960>"
            ]
          },
          "metadata": {},
          "execution_count": 277
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "full_model_losses = pd.DataFrame(full_model.history.history)\n",
        "full_model_losses.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "rWwr0nqRsVDb",
        "outputId": "2fe7ab5b-c762-4d48-9416-dcd1ff37e943"
      },
      "execution_count": 278,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 278
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "forecast = []\n",
        "first_eval_batch = scaled_train[-length:]\n",
        "current_batch = first_eval_batch.reshape((1,length,n_features))\n",
        "\n",
        "for i in range(20):\n",
        "  current_pred = model.predict(current_batch)[0]\n",
        "\n",
        "  forecast.append(current_pred)\n",
        "\n",
        "  current_batch = np.append(current_batch[:,1:,:],[[current_pred]],axis=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nvDnCx3AtLZz",
        "outputId": "eeff0aa0-3930-431a-a549-986d3a804ede"
      },
      "execution_count": 279,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#LSTM Model"
      ],
      "metadata": {
        "id": "Y8nSYHbNz70Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "early_stop = EarlyStopping(monitor='val_loss',patience=2)"
      ],
      "metadata": {
        "id": "0WEeDTfCyGcE"
      },
      "execution_count": 303,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "length"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1XLuzB5f0hfM",
        "outputId": "0ee61955-e087-4643-f051-e930ccb8bec5"
      },
      "execution_count": 304,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16"
            ]
          },
          "metadata": {},
          "execution_count": 304
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pz2yRk9-0rbP",
        "outputId": "dff38f47-f69c-4354-8233-19acdf8adef6"
      },
      "execution_count": 305,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(17, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 305
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "length = 16\n",
        "generator = TimeseriesGenerator(scaled_train,scaled_train,length = length,batch_size = 32)\n",
        "validation_generator = TimeseriesGenerator(scaled_test,scaled_test,length = length,batch_size = 32)"
      ],
      "metadata": {
        "id": "NnE6z0tU0vCg"
      },
      "execution_count": 336,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lstm_model = Sequential()\n",
        "lstm_model.add(LSTM(30,input_shape=(length,n_features)))\n",
        "lstm_model.add(Dropout(rate = 0.2))\n",
        "lstm_model.add(Dense(170))\n",
        "lstm_model.add(Dropout(rate = 0.6))\n",
        "lstm_model.add(Dense(500))\n",
        "lstm_model.add(Dropout(rate = 0.6))\n",
        "lstm_model.add(Dense(500))\n",
        "lstm_model.add(Dropout(rate = 0.398))\n",
        "lstm_model.add(Dense(500))\n",
        "lstm_model.add(Dropout(rate = 0.3))\n",
        "lstm_model.add(Dense(500))\n",
        "lstm_model.add(Dropout(rate = 0.4))\n",
        "lstm_model.add(Dense(290))\n",
        "lstm_model.add(Dropout(rate = 0.29))\n",
        "lstm_model.add(Dense(229))\n",
        "lstm_model.add(Dense(1))\n",
        "lstm_model.compile(optimizer='adam', loss='mse')"
      ],
      "metadata": {
        "id": "pBfajsIy1Szb"
      },
      "execution_count": 337,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lstm_model.fit_generator(generator,epochs=20,validation_data=validation_generator,shuffle=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bGPcTtSR1xil",
        "outputId": "12511b45-6c4f-4ed8-ec19-559ec2ee4e38"
      },
      "execution_count": 338,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-338-16f5b246bf59>:1: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  lstm_model.fit_generator(generator,epochs=20,validation_data=validation_generator,shuffle=False)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "15/15 [==============================] - 5s 41ms/step - loss: 0.1456 - val_loss: 0.0159\n",
            "Epoch 2/20\n",
            "15/15 [==============================] - 0s 11ms/step - loss: 0.0606 - val_loss: 0.0887\n",
            "Epoch 3/20\n",
            "15/15 [==============================] - 0s 11ms/step - loss: 0.0371 - val_loss: 0.0288\n",
            "Epoch 4/20\n",
            "15/15 [==============================] - 0s 10ms/step - loss: 0.0305 - val_loss: 0.0328\n",
            "Epoch 5/20\n",
            "15/15 [==============================] - 0s 11ms/step - loss: 0.0220 - val_loss: 0.0107\n",
            "Epoch 6/20\n",
            "15/15 [==============================] - 0s 10ms/step - loss: 0.0225 - val_loss: 0.0260\n",
            "Epoch 7/20\n",
            "15/15 [==============================] - 0s 11ms/step - loss: 0.0230 - val_loss: 0.0227\n",
            "Epoch 8/20\n",
            "15/15 [==============================] - 0s 11ms/step - loss: 0.0260 - val_loss: 0.0034\n",
            "Epoch 9/20\n",
            "15/15 [==============================] - 0s 10ms/step - loss: 0.0245 - val_loss: 8.2162e-05\n",
            "Epoch 10/20\n",
            "15/15 [==============================] - 0s 10ms/step - loss: 0.0264 - val_loss: 0.0031\n",
            "Epoch 11/20\n",
            "15/15 [==============================] - 0s 10ms/step - loss: 0.0159 - val_loss: 6.0422e-04\n",
            "Epoch 12/20\n",
            "15/15 [==============================] - 0s 10ms/step - loss: 0.0168 - val_loss: 8.2339e-04\n",
            "Epoch 13/20\n",
            "15/15 [==============================] - 0s 11ms/step - loss: 0.0129 - val_loss: 0.0013\n",
            "Epoch 14/20\n",
            "15/15 [==============================] - 0s 13ms/step - loss: 0.0178 - val_loss: 5.7298e-04\n",
            "Epoch 15/20\n",
            "15/15 [==============================] - 0s 11ms/step - loss: 0.0178 - val_loss: 4.6903e-04\n",
            "Epoch 16/20\n",
            "15/15 [==============================] - 0s 11ms/step - loss: 0.0190 - val_loss: 0.0287\n",
            "Epoch 17/20\n",
            "15/15 [==============================] - 0s 11ms/step - loss: 0.0338 - val_loss: 0.0429\n",
            "Epoch 18/20\n",
            "15/15 [==============================] - 0s 10ms/step - loss: 0.0674 - val_loss: 0.0133\n",
            "Epoch 19/20\n",
            "15/15 [==============================] - 0s 11ms/step - loss: 0.0499 - val_loss: 0.0020\n",
            "Epoch 20/20\n",
            "15/15 [==============================] - 0s 10ms/step - loss: 0.0571 - val_loss: 0.0015\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fb9ac39ef50>"
            ]
          },
          "metadata": {},
          "execution_count": 338
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "losses = pd.DataFrame(lstm_model.history.history)\n",
        "losses.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "QOrHuXYn2G4J",
        "outputId": "8398a929-76e6-4e9d-8dde-d61d5fe96c4b"
      },
      "execution_count": 339,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 339
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_predictions = []\n",
        "first_eval_batch = scaled_train[-length:]\n",
        "current_batch = first_eval_batch.reshape((1,length,n_features))\n",
        "\n",
        "for i in range(len(test)):\n",
        "  current_pred = lstm_model.predict(current_batch)[0]\n",
        "\n",
        "  test_predictions.append(current_pred)\n",
        "\n",
        "  current_batch = np.append(current_batch[:,1:,:],[[current_pred]],axis=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LbiAgOU73ChL",
        "outputId": "0a1fc011-fb21-43a5-ac3f-24fc711a9ff8"
      },
      "execution_count": 340,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 1s 1s/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 53ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 45ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 41ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "true_predictions = scaler.inverse_transform(test_predictions)\n",
        "test['LSTM_predictions'] = true_predictions\n",
        "test.plot(figsize=(12,8))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 821
        },
        "id": "wO1IPAi638GY",
        "outputId": "058efcc6-c499-468f-d49d-5edc33f7bae2"
      },
      "execution_count": 341,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-341-5512f3d84678>:2: 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",
            "  test['LSTM_predictions'] = true_predictions\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 341
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "forecast = []\n",
        "first_eval_batch = scaled_train[-length:]\n",
        "current_batch = first_eval_batch.reshape((1,length,n_features))\n",
        "\n",
        "for i in range(20):\n",
        "  current_pred = lstm_model.predict(current_batch)[0]\n",
        "\n",
        "  forecast.append(current_pred)\n",
        "\n",
        "  current_batch = np.append(current_batch[:,1:,:],[[current_pred]],axis=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pOm8qrrI4Vaq",
        "outputId": "f28d70d5-d000-4e15-fede-10651ac8023d"
      },
      "execution_count": 342,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "forecast = scaler.inverse_transform(forecast)\n",
        "full_data = pd.DataFrame(np.concatenate((df_array.flatten(),forecast.flatten())))\n",
        "full_data.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "BnL6Zs936yyL",
        "outputId": "1118660d-fb46-4598-e0f1-899bb865a0ee"
      },
      "execution_count": 343,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 343
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "forecast"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZyqW8Mgf6_8C",
        "outputId": "c52acf46-84d8-4790-911a-8b5bca23f9f5"
      },
      "execution_count": 344,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[806.48305443],\n",
              "       [811.91531013],\n",
              "       [817.04688682],\n",
              "       [821.87794116],\n",
              "       [826.4254713 ],\n",
              "       [830.72688096],\n",
              "       [834.79924661],\n",
              "       [838.63675207],\n",
              "       [842.2654037 ],\n",
              "       [845.68232279],\n",
              "       [848.89634132],\n",
              "       [851.94482386],\n",
              "       [854.84371104],\n",
              "       [857.57868763],\n",
              "       [860.17043338],\n",
              "       [862.61702915],\n",
              "       [864.93288811],\n",
              "       [867.10904118],\n",
              "       [869.15872656],\n",
              "       [871.08821083]])"
            ]
          },
          "metadata": {},
          "execution_count": 344
        }
      ]
    }
  ]
}