Forcasting_Model.ipynb

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      "source": [
        "import tensorflow as tf\n",
        "import os\n",
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df= pd.read_csv(\"/content/drive/MyDrive/Nasa_Space_Apps/data.csv\",index_col=0)\n",
        "df= df.drop([\"Range\",\"Label\"],axis=1)\n",
        "df=df.drop(\"Unnamed: 0.1\", axis=1)\n",
        "df[\"time\"]= pd.to_datetime(df[\"time\"])\n",
        "df.index= df[\"time\"]\n",
        "df=df.drop(\"time\",axis=1)\n",
        "df"
      ],
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        "df['Seconds'] = df.index.map(pd.Timestamp.timestamp)\n",
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              "                        BGSM1     BGSM2     BGSM3     BGSE1     BGSE2  \\\n",
              "time                                                                    \n",
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              "      <th>2021-10-02 22:40:00</th>\n",
              "      <td>-2.842662</td>\n",
              "      <td>2.096434</td>\n",
              "      <td>1.483598</td>\n",
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              "      <td>1.633214e+09</td>\n",
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              "    <tr>\n",
              "      <th>2021-10-02 23:00:00</th>\n",
              "      <td>-2.677102</td>\n",
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              "      <td>1.720208</td>\n",
              "      <td>-2.677079</td>\n",
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              "      <td>1.633216e+09</td>\n",
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              "    <tr>\n",
              "      <th>2021-10-02 23:20:00</th>\n",
              "      <td>-2.786466</td>\n",
              "      <td>2.344785</td>\n",
              "      <td>1.557776</td>\n",
              "      <td>-2.786444</td>\n",
              "      <td>2.646010</td>\n",
              "      <td>0.961490</td>\n",
              "      <td>1.633217e+09</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>32652 rows × 7 columns</p>\n",
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              "  "
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "day = 60*60*24\n",
        "year = 365.2425*day\n",
        "\n",
        "df['day_sin'] = np.sin(df['Seconds'] * (2* np.pi / day))\n",
        "df['day_cos'] = np.cos(df['Seconds'] * (2 * np.pi / day))\n",
        "df['year_sin'] = np.sin(df['Seconds'] * (2 * np.pi / year))\n",
        "df['year_cos'] = np.cos(df['Seconds'] * (2 * np.pi / year))\n",
        "df.head()"
      ],
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          "height": 238
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        "id": "mGMfgD3ZHI_6",
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              "                        BGSM1     BGSM2     BGSM3     BGSE1     BGSE2  \\\n",
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              "                        BGSE3       Seconds       day_sin   day_cos  year_sin  \\\n",
              "time                                                                            \n",
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              "\n",
              "                     year_cos  \n",
              "time                           \n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop('Seconds', axis=1)\n",
        "df.head()"
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              "                        BGSM1     BGSM2     BGSM3     BGSE1     BGSE2  \\\n",
              "time                                                                    \n",
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              "\n",
              "                        BGSE3       day_sin   day_cos  year_sin  year_cos  \n",
              "time                                                                       \n",
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              "      <td>-3.180378</td>\n",
              "      <td>-4.026194</td>\n",
              "      <td>2.446195</td>\n",
              "      <td>-3.416094</td>\n",
              "      <td>-1.026605e-11</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.499097</td>\n",
              "      <td>-0.866546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003-08-02 00:20:00</th>\n",
              "      <td>-4.817634</td>\n",
              "      <td>1.867184</td>\n",
              "      <td>-2.034130</td>\n",
              "      <td>-4.817625</td>\n",
              "      <td>1.660636</td>\n",
              "      <td>-2.207846</td>\n",
              "      <td>8.715574e-02</td>\n",
              "      <td>0.996195</td>\n",
              "      <td>-0.499304</td>\n",
              "      <td>-0.866427</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003-08-02 00:40:00</th>\n",
              "      <td>-5.015400</td>\n",
              "      <td>1.871090</td>\n",
              "      <td>-0.376598</td>\n",
              "      <td>-5.015398</td>\n",
              "      <td>1.818871</td>\n",
              "      <td>-0.571951</td>\n",
              "      <td>1.736482e-01</td>\n",
              "      <td>0.984808</td>\n",
              "      <td>-0.499511</td>\n",
              "      <td>-0.866308</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003-08-02 01:00:00</th>\n",
              "      <td>-5.219594</td>\n",
              "      <td>1.665034</td>\n",
              "      <td>-0.166770</td>\n",
              "      <td>-5.219593</td>\n",
              "      <td>1.634691</td>\n",
              "      <td>-0.357167</td>\n",
              "      <td>2.588190e-01</td>\n",
              "      <td>0.965926</td>\n",
              "      <td>-0.499718</td>\n",
              "      <td>-0.866188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003-08-02 01:20:00</th>\n",
              "      <td>-4.009341</td>\n",
              "      <td>2.692525</td>\n",
              "      <td>-0.827312</td>\n",
              "      <td>-4.009337</td>\n",
              "      <td>2.568638</td>\n",
              "      <td>-1.156507</td>\n",
              "      <td>3.420201e-01</td>\n",
              "      <td>0.939693</td>\n",
              "      <td>-0.499924</td>\n",
              "      <td>-0.866069</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ee79a1c0-1ac6-45df-b196-deb301a11e4b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ee79a1c0-1ac6-45df-b196-deb301a11e4b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ee79a1c0-1ac6-45df-b196-deb301a11e4b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def df_to_X_y3(df, window_size=70):\n",
        "  df_as_np = df.to_numpy()\n",
        "  X = []\n",
        "  y = []\n",
        "  time=[]\n",
        "  for i in range(len(df_as_np)-window_size):\n",
        "    row = [r for r in df_as_np[i:i+window_size]]\n",
        "    row_time = [r for r in df.index[i:i+window_size]]\n",
        "    X.append(row)\n",
        "    time.append(row_time)\n",
        "    label = df_as_np[i+window_size][:6]\n",
        "    y.append(label)\n",
        "  return np.array(X), np.array(y), np.array(time)"
      ],
      "metadata": {
        "id": "x-afDjYppMKQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X,y,time = df_to_X_y3(df)"
      ],
      "metadata": {
        "id": "gxxWfoGdpbZw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape, y.shape, time.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PEDpsSF3Hq6x",
        "outputId": "9acfe356-4262-429d-82ac-2e56b67422a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((32582, 70, 10), (32582, 6), (32582, 70))"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, y_train = X[:27000], y[:27000]\n",
        "X_test, y_test, time_test = X[27000:], y[27000:], time[27000:]\n",
        "X_train.shape, y_train.shape,  X_test.shape, y_test.shape, time_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9jbRSPeNqQbd",
        "outputId": "c3ac85c7-8cd6-4fcf-ebd1-82c6296b7a2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((27000, 70, 10), (27000, 6), (5582, 70, 10), (5582, 6), (5582, 70))"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "BGSM1_training_mean = np.mean(X_train[:, :, 0])\n",
        "BGSM1_training_std = np.std(X_train[:, :, 0])\n",
        "\n",
        "BGSM2_training_mean = np.mean(X_train[:, :, 1])\n",
        "BGSM2_training_std = np.std(X_train[:, :, 1])\n",
        "\n",
        "BGSM3_training_mean = np.mean(X_train[:, :, 2])\n",
        "BGSM3_training_std = np.std(X_train[:, :, 2])\n",
        "\n",
        "BGSM4_training_mean = np.mean(X_train[:, :, 3])\n",
        "BGSM4_training_std = np.std(X_train[:, :, 3])\n",
        "\n",
        "BGSM5_training_mean = np.mean(X_train[:, :, 4])\n",
        "BGSM5_training_std = np.std(X_train[:, :, 4])\n",
        "\n",
        "BGSM6_training_mean = np.mean(X_train[:, :, 5])\n",
        "BGSM6_training_std = np.std(X_train[:, :, 5])\n",
        "\n",
        "\n",
        "\n",
        "def preprocess(X):\n",
        "  X[:, :, 0] = (X[:, :, 0] - BGSM1_training_mean) / BGSM1_training_std\n",
        "  X[:, :, 1] = (X[:, :, 1] - BGSM2_training_mean) / BGSM2_training_std\n",
        "  X[:, :, 2] = (X[:, :, 2] - BGSM3_training_mean) / BGSM3_training_std\n",
        "  X[:, :, 3] = (X[:, :, 3] - BGSM4_training_mean) / BGSM4_training_std\n",
        "  X[:, :, 4] = (X[:, :, 4] - BGSM5_training_mean) / BGSM5_training_std\n",
        "  X[:, :, 5] = (X[:, :, 5] - BGSM6_training_mean) / BGSM6_training_std\n",
        "\n",
        "def preprocess_output(y):\n",
        "  y[:, 0] = (y[:, 0] - BGSM1_training_mean) / BGSM1_training_std\n",
        "  y[:, 1] = (y[:, 1] - BGSM2_training_mean) / BGSM2_training_std\n",
        "  y[:, 2] = (y[:, 2] - BGSM3_training_mean) / BGSM3_training_std\n",
        "  y[:, 3] = (y[:, 3] - BGSM4_training_mean) / BGSM4_training_std\n",
        "  y[:, 4] = (y[:, 4] - BGSM5_training_mean) / BGSM5_training_std\n",
        "  y[:, 5] = (y[:, 5] - BGSM6_training_mean) / BGSM6_training_std\n",
        "  return y"
      ],
      "metadata": {
        "id": "Q_pMNTRMqSFc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "preprocess(X_train)\n",
        "preprocess(X_test)"
      ],
      "metadata": {
        "id": "w1Dd5GXRsVMU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "preprocess_output(y_train)\n",
        "preprocess_output(y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i-WcO8Xesaog",
        "outputId": "ea8ed9a5-d981-4153-e55a-c3f3063788ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.14659609, -0.06901891,  0.16948943,  0.14658954, -0.0699851 ,\n",
              "         0.23083064],\n",
              "       [ 0.15040765, -0.06435641,  0.11973062,  0.15040313, -0.0657079 ,\n",
              "         0.179046  ],\n",
              "       [ 0.04693421, -0.12747066,  0.17228375,  0.04692761, -0.13379809,\n",
              "         0.22344981],\n",
              "       ...,\n",
              "       [-0.9102867 ,  0.70770001,  0.4449055 , -0.9102811 ,  0.80530912,\n",
              "         0.35281326],\n",
              "       [-0.86774629,  0.713551  ,  0.51346003, -0.86773988,  0.82574985,\n",
              "         0.41524675],\n",
              "       [-0.89584712,  0.77056375,  0.46639772, -0.89584125,  0.87356857,\n",
              "         0.35438121]])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import *\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping\n",
        "from tensorflow.keras.losses import MeanSquaredError\n",
        "from tensorflow.keras.metrics import RootMeanSquaredError\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "model = Sequential()\n",
        "model.add(InputLayer((70, 10)))\n",
        "model.add(LSTM(256, return_sequences=True))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128,\"relu\"))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(8, 'relu'))\n",
        "model.add(Dense(6, 'linear'))\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UVMX3QXHseHG",
        "outputId": "1bfabc2b-73b3-4ca4-dfc0-3c849a43b00e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm (LSTM)                 (None, 70, 256)           273408    \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 17920)             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 128)               2293888   \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 128)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 8)                 1032      \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 6)                 54        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2,568,382\n",
            "Trainable params: 2,568,382\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cp = ModelCheckpoint('model5/', save_best_only=True)\n",
        "es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=10)\n",
        "model.compile(loss=MeanSquaredError(), optimizer=Adam(learning_rate=0.0001), metrics=[RootMeanSquaredError()])"
      ],
      "metadata": {
        "id": "cT1TVHcss0Wg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=50, batch_size= 70,callbacks=[cp,es])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "H_p3SDCHtL68",
        "outputId": "5cff07b0-5236-4e8e-ee35-d24a08688104"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "386/386 [==============================] - ETA: 0s - loss: 0.7398 - root_mean_squared_error: 0.8601"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f8bc6573810> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r386/386 [==============================] - 144s 368ms/step - loss: 0.7398 - root_mean_squared_error: 0.8601 - val_loss: 0.5276 - val_root_mean_squared_error: 0.7264\n",
            "Epoch 2/50\n",
            "386/386 [==============================] - ETA: 0s - loss: 0.6049 - root_mean_squared_error: 0.7777"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f8bc6573810> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r386/386 [==============================] - 141s 365ms/step - loss: 0.6049 - root_mean_squared_error: 0.7777 - val_loss: 0.4472 - val_root_mean_squared_error: 0.6688\n",
            "Epoch 3/50\n",
            "386/386 [==============================] - ETA: 0s - loss: 0.5291 - root_mean_squared_error: 0.7274"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f8bc6573810> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r386/386 [==============================] - 140s 362ms/step - loss: 0.5291 - root_mean_squared_error: 0.7274 - val_loss: 0.3704 - val_root_mean_squared_error: 0.6086\n",
            "Epoch 4/50\n",
            "386/386 [==============================] - ETA: 0s - loss: 0.4643 - root_mean_squared_error: 0.6814"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f8bc6573810> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r386/386 [==============================] - 140s 362ms/step - loss: 0.4643 - root_mean_squared_error: 0.6814 - val_loss: 0.3303 - val_root_mean_squared_error: 0.5747\n",
            "Epoch 5/50\n",
            "386/386 [==============================] - ETA: 0s - loss: 0.4158 - root_mean_squared_error: 0.6448"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f8bc6573810> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r386/386 [==============================] - 139s 361ms/step - loss: 0.4158 - root_mean_squared_error: 0.6448 - val_loss: 0.2826 - val_root_mean_squared_error: 0.5316\n",
            "Epoch 6/50\n",
            "386/386 [==============================] - ETA: 0s - loss: 0.3806 - root_mean_squared_error: 0.6170"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f8bc6573810> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r386/386 [==============================] - 138s 359ms/step - loss: 0.3806 - root_mean_squared_error: 0.6170 - val_loss: 0.2540 - val_root_mean_squared_error: 0.5040\n",
            "Epoch 7/50\n",
            "386/386 [==============================] - ETA: 0s - loss: 0.3555 - root_mean_squared_error: 0.5962"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f8bc6573810> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r386/386 [==============================] - 139s 361ms/step - loss: 0.3555 - root_mean_squared_error: 0.5962 - val_loss: 0.2516 - val_root_mean_squared_error: 0.5016\n",
            "Epoch 8/50\n",
            "386/386 [==============================] - ETA: 0s - loss: 0.3306 - root_mean_squared_error: 0.5750"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f8bc6573810> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r386/386 [==============================] - 139s 361ms/step - loss: 0.3306 - root_mean_squared_error: 0.5750 - val_loss: 0.2425 - val_root_mean_squared_error: 0.4924\n",
            "Epoch 9/50\n",
            "386/386 [==============================] - ETA: 0s - loss: 0.3224 - root_mean_squared_error: 0.5678"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f8bc6573810> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r386/386 [==============================] - 139s 361ms/step - loss: 0.3224 - root_mean_squared_error: 0.5678 - val_loss: 0.2356 - val_root_mean_squared_error: 0.4854\n",
            "Epoch 10/50\n",
            "386/386 [==============================] - ETA: 0s - loss: 0.3116 - root_mean_squared_error: 0.5582"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f8bc6573810> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r386/386 [==============================] - 141s 365ms/step - loss: 0.3116 - root_mean_squared_error: 0.5582 - val_loss: 0.2258 - val_root_mean_squared_error: 0.4752\n",
            "Epoch 11/50\n",
            "386/386 [==============================] - ETA: 0s - loss: 0.3037 - root_mean_squared_error: 0.5510"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f8bc6573810> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r386/386 [==============================] - 139s 361ms/step - loss: 0.3037 - root_mean_squared_error: 0.5510 - val_loss: 0.2223 - val_root_mean_squared_error: 0.4715\n",
            "Epoch 12/50\n",
            "386/386 [==============================] - ETA: 0s - loss: 0.2932 - root_mean_squared_error: 0.5415"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f8bc6573810> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r386/386 [==============================] - 143s 372ms/step - loss: 0.2932 - root_mean_squared_error: 0.5415 - val_loss: 0.2195 - val_root_mean_squared_error: 0.4685\n",
            "Epoch 13/50\n",
            "386/386 [==============================] - ETA: 0s - loss: 0.2875 - root_mean_squared_error: 0.5362"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f8bc6573810> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r386/386 [==============================] - 147s 382ms/step - loss: 0.2875 - root_mean_squared_error: 0.5362 - val_loss: 0.2152 - val_root_mean_squared_error: 0.4639\n",
            "Epoch 14/50\n",
            "386/386 [==============================] - ETA: 0s - loss: 0.2861 - root_mean_squared_error: 0.5349"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f8bc6573810> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r386/386 [==============================] - 145s 376ms/step - loss: 0.2861 - root_mean_squared_error: 0.5349 - val_loss: 0.2141 - val_root_mean_squared_error: 0.4627\n",
            "Epoch 15/50\n",
            "386/386 [==============================] - 138s 358ms/step - loss: 0.2827 - root_mean_squared_error: 0.5317 - val_loss: 0.2159 - val_root_mean_squared_error: 0.4646\n",
            "Epoch 16/50\n",
            "386/386 [==============================] - 138s 358ms/step - loss: 0.2762 - root_mean_squared_error: 0.5256 - val_loss: 0.2171 - val_root_mean_squared_error: 0.4660\n",
            "Epoch 17/50\n",
            "386/386 [==============================] - ETA: 0s - loss: 0.2704 - root_mean_squared_error: 0.5200"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f8bc6573810> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r386/386 [==============================] - 141s 365ms/step - loss: 0.2704 - root_mean_squared_error: 0.5200 - val_loss: 0.2139 - val_root_mean_squared_error: 0.4625\n",
            "Epoch 18/50\n",
            "386/386 [==============================] - ETA: 0s - loss: 0.2671 - root_mean_squared_error: 0.5169"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as lstm_cell_layer_call_fn, lstm_cell_layer_call_and_return_conditional_losses while saving (showing 2 of 2). These functions will not be directly callable after loading.\n",
            "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f8bc6573810> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r386/386 [==============================] - 142s 368ms/step - loss: 0.2671 - root_mean_squared_error: 0.5169 - val_loss: 0.2097 - val_root_mean_squared_error: 0.4579\n",
            "Epoch 19/50\n",
            "386/386 [==============================] - 141s 366ms/step - loss: 0.2663 - root_mean_squared_error: 0.5161 - val_loss: 0.2122 - val_root_mean_squared_error: 0.4607\n",
            "Epoch 20/50\n",
            "386/386 [==============================] - 137s 356ms/step - loss: 0.2658 - root_mean_squared_error: 0.5155 - val_loss: 0.2132 - val_root_mean_squared_error: 0.4617\n",
            "Epoch 21/50\n",
            "386/386 [==============================] - 138s 357ms/step - loss: 0.2630 - root_mean_squared_error: 0.5128 - val_loss: 0.2114 - val_root_mean_squared_error: 0.4597\n",
            "Epoch 22/50\n",
            "386/386 [==============================] - 138s 358ms/step - loss: 0.2558 - root_mean_squared_error: 0.5058 - val_loss: 0.2108 - val_root_mean_squared_error: 0.4591\n",
            "Epoch 23/50\n",
            "386/386 [==============================] - 138s 356ms/step - loss: 0.2610 - root_mean_squared_error: 0.5109 - val_loss: 0.2135 - val_root_mean_squared_error: 0.4620\n",
            "Epoch 24/50\n",
            "386/386 [==============================] - 138s 357ms/step - loss: 0.2515 - root_mean_squared_error: 0.5015 - val_loss: 0.2098 - val_root_mean_squared_error: 0.4581\n",
            "Epoch 25/50\n",
            "386/386 [==============================] - 137s 356ms/step - loss: 0.2515 - root_mean_squared_error: 0.5015 - val_loss: 0.2106 - val_root_mean_squared_error: 0.4589\n",
            "Epoch 26/50\n",
            "373/386 [===========================>..] - ETA: 4s - loss: 0.2507 - root_mean_squared_error: 0.5007"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-15-23b2aebce3c9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;36m70\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcp\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mes\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1382\u001b[0m                 _r=1):\n\u001b[1;32m   1383\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1384\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1385\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1386\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    913\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    914\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 915\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    916\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    917\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    945\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    946\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 947\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    948\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    949\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2955\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[1;32m   2956\u001b[0m     return graph_function._call_flat(\n\u001b[0;32m-> 2957\u001b[0;31m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[0m\u001b[1;32m   2958\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2959\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1852\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1853\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1854\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1855\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1856\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    502\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    503\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 504\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    505\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    506\u001b[0m           outputs = execute.execute_with_cancellation(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     53\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 55\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     56\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def postprocess_BGSM1(arr):\n",
        "  arr = (arr*BGSM1_training_std) + BGSM1_training_mean\n",
        "  return arr\n",
        "def postprocess_BGSM2(arr):\n",
        "  arr = (arr*BGSM2_training_std) + BGSM2_training_mean\n",
        "  return arr\n",
        "def postprocess_BGSM3(arr):\n",
        "  arr = (arr*BGSM3_training_std) + BGSM3_training_mean\n",
        "  return arr\n",
        "def postprocess_BGSM4(arr):\n",
        "  arr = (arr*BGSM4_training_std) + BGSM4_training_mean\n",
        "  return arr\n",
        "def postprocess_BGSM5(arr):\n",
        "  arr = (arr*BGSM5_training_std) + BGSM5_training_mean\n",
        "  return arr\n",
        "def postprocess_BGSM6(arr):\n",
        "  arr = (arr*BGSM6_training_std) + BGSM6_training_mean\n",
        "  return arr\n",
        "\n"
      ],
      "metadata": {
        "id": "NgnpkaFMxQ9J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "def plot_predictions2(model, X, y, start=0, end=100):\n",
        "  predictions = model.predict(X)\n",
        "  BGSM1_preds, BGSM2_preds, BGSM3_preds, BGSM4_preds, BGSM5_preds, BGSM6_preds = postprocess_BGSM1(predictions[:, 0]), postprocess_BGSM2(predictions[:, 1]), postprocess_BGSM3(predictions[:, 2]), postprocess_BGSM4(predictions[:, 3]), postprocess_BGSM5(predictions[:, 4]), postprocess_BGSM6(predictions[:, 5])\n",
        "  BGSM1_actuals, BGSM2_actuals, BGSM3_actuals, BGSM4_actuals, BGSM5_actuals, BGSM6_actuals = postprocess_BGSM1(y[:, 0]), postprocess_BGSM2(y[:, 1]), postprocess_BGSM3(y[:, 2]), postprocess_BGSM4(y[:, 3]), postprocess_BGSM5(y[:, 4]), postprocess_BGSM6(y[:, 5])\n",
        "  df = pd.DataFrame(data={'BGSM1 Predictions': BGSM1_preds,\n",
        "                          'BGSM1 Actuals':BGSM1_actuals,\n",
        "                          'BGSM2 Predictions': BGSM2_preds,\n",
        "                          'Pressure Actuals': BGSM2_actuals\n",
        "                          })\n",
        "  plt.plot(df['BGSM1 Predictions'][start:end])\n",
        "  plt.plot(df['BGSM1 Actuals'][start:end])\n",
        "  return df[start:end]"
      ],
      "metadata": {
        "id": "0dF-xXzvtSI7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df4= plot_predictions2(model,X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "qx18A4sowhr5",
        "outputId": "37414c85-8e0c-4b4b-acd7-641626ab4eae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save(\"forcasting_model.h5\")"
      ],
      "metadata": {
        "id": "tJs3h_Pi604p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yc3eHObaF1UY",
        "outputId": "04ceef42-ec2b-4f97-b939-5d8c42d95835"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5582, 70, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "example=X_test[70]\n",
        "example_time= time_test[70]\n",
        "example.shape, example_time.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yp1L3w50wtyZ",
        "outputId": "2165ea07-792e-417f-dcbc-bd3272a8ed24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((70, 10), (70,))"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "example_time=np.array(pd.DataFrame(example_time)[0].map(pd.Timestamp.timestamp))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5OVPxZlrNSIf",
        "outputId": "1caf2735-165c-4045-99ab-5c87d9f0551d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.6225560e+09, 1.6225572e+09, 1.6225584e+09, 1.6225596e+09,\n",
              "       1.6225608e+09, 1.6225620e+09, 1.6225632e+09, 1.6225644e+09,\n",
              "       1.6225656e+09, 1.6225668e+09, 1.6225680e+09, 1.6225692e+09,\n",
              "       1.6225704e+09, 1.6225716e+09, 1.6225728e+09, 1.6225740e+09,\n",
              "       1.6225752e+09, 1.6225764e+09, 1.6225776e+09, 1.6225788e+09,\n",
              "       1.6225800e+09, 1.6225812e+09, 1.6225824e+09, 1.6225836e+09,\n",
              "       1.6225848e+09, 1.6225860e+09, 1.6225872e+09, 1.6225884e+09,\n",
              "       1.6225896e+09, 1.6202592e+09, 1.6202604e+09, 1.6202616e+09,\n",
              "       1.6202628e+09, 1.6202640e+09, 1.6202652e+09, 1.6202664e+09,\n",
              "       1.6202676e+09, 1.6202688e+09, 1.6202700e+09, 1.6202712e+09,\n",
              "       1.6202724e+09, 1.6202736e+09, 1.6202748e+09, 1.6202760e+09,\n",
              "       1.6202772e+09, 1.6202784e+09, 1.6202796e+09, 1.6202808e+09,\n",
              "       1.6202820e+09, 1.6202832e+09, 1.6202844e+09, 1.6202856e+09,\n",
              "       1.6202868e+09, 1.6202880e+09, 1.6202892e+09, 1.6202904e+09,\n",
              "       1.6202916e+09, 1.6202928e+09, 1.6202940e+09, 1.6202952e+09,\n",
              "       1.6202964e+09, 1.6202976e+09, 1.6202988e+09, 1.6203000e+09,\n",
              "       1.6203012e+09, 1.6203024e+09, 1.6203036e+09, 1.6203048e+09,\n",
              "       1.6203060e+09, 1.6203072e+09])"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(70):\n",
        "  prediction= model.predict(example[i:].reshape(1,70,10))\n",
        "  example= np.append(example, prediction).reshape(70+i+1, 6)\n"
      ],
      "metadata": {
        "id": "j7G0QBO0Epwo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2= pd.DataFrame(example)\n",
        "df3= pd.DataFrame(example[:70])\n",
        "plt.plot(df2[0])\n",
        "plt.plot(df3[0])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "NN74DariE_7Z",
        "outputId": "35d8ca7b-e0ae-4919-9eb3-44cc99a380b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f8154b9d310>]"
            ]
          },
          "metadata": {},
          "execution_count": 182
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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6QSl1SCl1VCl1Z5rz71VKtSmldjk/f1CIcYX0dP7mW0S1hzVvuCPva92OhR8fJ8NlZTJzrdfhxIzck7Gl8MQ66TWq8rpmNIbfTncQ7stNcHva7LxD3pomuPUu9mz9hxFZRPNFKTuL5v7zE98LIAiFZtKCr5RyAV8BbgTWArcppdJtT7xHa32p8/OtyY4rpCca7ueSjl+xr/pqqmvz22UL4HYs/GyCn6r1mkuMf1z5cZm57dxN4Yt3E3YSp00UV8C+PpKj4Ie77Nj/QM08lq67gq1v+aNJjQ+wrrmag5JFU5hBFMLC3wYc1Vof11rHgR8CNxegXyEL4f4envuvv6b1s0t54d5/GTy+79H/poow/iveO6F+vUHbsk5GMwt+X28XQRXLWus1RcLw484xN0+KYLKHqKcmr2tG4w3Zgh/r786pfazbFvyK+vz2LWRjXXMVsaTF8XbJoinMDAoh+POBM8Pen3WOjeZWpdQepdS9Sqm08XRKqfcrpXYopXa0tbUVYGrlyd4nf8bAlzax/fi/06C74NyOwXPWiSfpooq12980ob69TihlMkvCs66L9n+3u2psLdvRJF3+nHPzpKi0ekj4Mkf/5ILHEfz4QG4+fLPPXlytznFnbS6sa7bnsF+qYQkzhKlatH0AWKK13gg8AnwvXSOt9V1a661a662NjeP7h2cblmny7Hc+zrpf386AUcGBG3/ECfdSvLHOwTbeaAedroaMC57j4Q2k0iFktkr7nMIqoytppSPpCuQl+HbitD7MwPgRQNkIhOxvCIlwbhY+A+1EtYeKysl9sxjO8sYQXrfB/nPixxdmBoUQ/HPAcIt9gXNsEK11h9Y65rz9FrClAOPOOvY9eR9Xnv4GL1Vfx5w/f5pLrngjA55aAvEhwQ8mOhnwTFwsAyHbpaOzCP5grdeG8Xfxmi4/3hxy86To7+vGo0xUcPwIoGwEqux7YEZyE1tXpI0uVZNXKunxcLsM1jRVysKtMGMoxG/3i8BKpdRSpZQXeCdw//AGSqnh3/1vAkZWpBZyIt5nu7mabvocwQrHZeGto9IcWpisSnYR801cLFPl/KwsCc+SvXYIY11T5l22KbQ7gG/wWT8+vR22a2V04rR8CTqWuhXNTWx9sQ763JNzI6VjXXMVr1zolXTJwoxg0oKvtU4Cfwo8jC3kP9Ja71dK/Y1S6ian2YeVUvuVUruBDwPvney4sxHLtDcRGW7P4LFkoIEay/YRa8uiRndjBiYulj5/EEur7Jul+loIa19O7g/LHcxL8Ae6bcH3Vk1S8ENV9ufIUfBDiS7Ck/hmlIm1zdX0RBKc685v4VoQikFBUvlprR8CHhp17NPDXn8S+GQhxprVmPY2fbfHO3Qs1EhQxQj392CaJpUqARUTX/9QhkEYHyqL4Nu7bGtZkIP7Q3sCBIhhWRrDUOO2H0ycVjU2j04+GC4XvSoA8dyqbVWZXXT4J5ZKIRvrmm0X2f7zvSyoDRa8f0HIB9lpW0Joy7HwXUPPaaPSFsau1vN0O5uHXDmES2YjovxZBd8fa6M3x0payhPEp5LE4vGc2qcSp1XUTe4zAIQJ4spB8C3TpFb3YAYLHyhwSVMVhkL8+MKMQAS/hNCOhe8ZZuH7qm1h7Os8T3+HvZjqr0lfCSpXosqPK5lZ8KsSHUR8ObpcnGRs4XBuxUhSPnd/aHIbrwAiRghXYvxxezpbcSsLFSq84Ae8LlbOqeRlSbEgzABE8EsJa6wPP1hnr4dHuy4SdTYPBWsnJ/jxcQS/zuokEczN5WLkmH0zReqh5vZmzoSZKzFXCE8OVa962+0Hpbt68t8q0rF9WR07TnYRT8qOW2F6EcEvIbSzaOseJviV9bbgx3taiPc4m4caxo+Pz0bcFcy4O7a/t4uQikJFbg8Vw+ukasiyc3c4KcH3TiKPTYq4K4TPHH+Xa19nYb4ZZeLK5Q1EEiZ7zua4J0AQioQIfilhjV20rWmwBd/sb8Xqa8XSiprG8XfAZiNh+PFkyH/TddHedOWqzm2Mwdw84RzTC5i2r3/EwvQESXgq8OdQBCX1zaiibnL3LRPbl9WhFDxzrKMo/QtCrojglxKWCYy08P2BEH06gBpoxwi30aMqJy2WSVcAn5Xewu91FoYDdbl9i3D5bZdOIkcLP+W2Gv4ZJ4rpqSBgjZ+LP9lrRwZN9ptRJmqCXtY1V/HMsfai9C8IuSKCX0qYCZLaGLMbtNuoxRNtxxPtoMeY/GKn6Q7gzVAtKtJp17KtzLF0osdJt5yI5mrhJ0hoV0F2vFqeCkJ6fMHX/a0ktUF13eRCQbPxquUNvHSqm2jCLNoYgjAeIvilhGViMjZHTr+7Bn+sk0C8gwH35DcPmZ5Qxs1SqVq2NVlq2Q5nKBlbboKvzDiJwmwPQfurCaoYyUT2kFBXuI0uVT3h/EO5cOXyeuKmxc5TEq0jTB8i+CWEshKYaf7Lot46QskuKpNdRCeRViGFdgcz1oPVAx0ktUFVTW7jeP224JvxHC18K0lSFUZ4lc+petWbXWS9sU56XYVLmpaOy5fU4TaUuHWEaUUEv5SwkiTVWOs37q+n2uqm2uomMYm0CoN4gviJo62xYYRGvI9+FczZ5eINOoKfJRnbcOyHWmEsfCNg73Id6MseHROMd0wq4VwuVPjcbFpYIwu3wrQigl9CKJ3epWMFG6mll0oVgULsFvWGMJQmGhkr0q54H2EVyrkrvyP4Op5bIXNlJQrm0vEE7fWM6DhFUConmXAuV65YWsfesz2E47kVVheEQiOCX0IoK0EyjeAbw3LnpFItTGocny3okTSFzN3JfiJG7jlh8hf8ZNqH2kRwB2w3TbQ/s0snMtBHve4kGSpOSOZwrlhWT9LSvHxa4vGF6UEEv5TIsGjrHpY7x1uAzUPK2SyVbnesJ9lPzFWRc18up6+s2TeHj20lMdO4rSaCr2L8IihHdz6GV5kEV15VkDGzsWVxLYaC54+LW0eYHkTwSwjDSmClWdD01wwJfrB28paqywmljEXGCr7fHCDuyt2lg2EQxQOJ3NIDGzpRMMH3OzUDEuHMicv6Dz1GUhss33J9QcbMRoXPzfr51Tx/onP8xoJQBETwS4hMPvzhhbcr6ydv4budUMp4OI3gWwMkPblb+AAxfBhZcvMMR2VYmJ4IwQq7oIkZzlxTtvbi8xzzrKKiqvDFT9KxbUkdL5/pJpaUeHxh6hHBLyGUTmKmsfBT6RUAaufktiEqGx4nlDKRxsIP6jCmtyqv/qLKj5HM0cK3ElgFEvxQtR15ozMUQRno62Z54jCdc64oyHi5sG1pHfGkxZ6zUthcmHpE8EuITP7typoGEtpFnw7gD+ThbsmAJ8NmKW1ZhHQYy5ufhR9XfoxkbnVtDZ0sWFimPxAiqQ10LL3gH9vxKB5lUrHmdQUZLxcuX2I/hF4Qt44wDYjglxCGNrHSRem4XHSparqNwrglfEF7w1JyVF3bWDSMV5ngy8/CTxi+jNk3R+PSSSyjMIKvDIMBFcCIpU/NPHD4ceLaxYop8N+nqA15WT23Uvz4wrQggl9CKJ3M6O7oc9XQ7y7MbtGU4FujNkv1OztWDX+egu8K4LZytPALGKUDMKBCGBmKoNS3vcAx75rBwu1Txbaldew82Sl5dYQpRwS/hDC0mTZKB6D/1Z8gedXHCzJOqtqUFR1pGUf6bKvUFcgvQVvS8OPJUfBdZH6oTYSoCuJOjLXw+3o6WZ44Qs/c7QUbK1du3NDEQNzk/l3np3xsYXYjgl9CZBP8Tde+kw2vvaUg41RU2t8URi92RvvthUZ3MD/BN90BvLla+DqJZUw+NXKKmCuENzl2x3DnhZO4lMbdtKZgY+XKlcvqWdNUyXeePoHWevD4mc4wb/vq03zyf/eI9S8UBRH8EsKwCmv9ZsLldts59mMjI0lizo5Vbyg/15HpCmTMvjkat06iC+TDB4i7K/CZY1068Yh9zO2fWncOgFKK33/1Ug629PGsswnr5dNd3PLVpznY0sfdL5zhHV9/lrNduYWyCkKuiOCXEAaZLfxC068qcI2KbkntWPXlWWBcu3MXfJdOYqnCWfhJdzBtMZchwZ98VNNEuOnSZupCXr76+DH+5oFX+J1vPEfA6+L+P30N33zPVk62D3Dtl57gz3+0iwMXMm8cE4R8EMEvIVwFdndkI+yqwJ0YLfj2+0BlfpkltSeAn+gI90UmXAW28E13CH+aVM9JpwJXas/BVOP3uPjdKxbx1NF2vvvMCW65bD4//ZNXs2JOBa9fO5eHPnIVt21byC/3tfDWrzzN0dYcK4YJQhaK7x8QCoahTfQUWfhRVwXe5MjFTitiu3iClXlGA3mCBIgTS1r4Pdnn78IsqOBb3goCeuz6QdJZkPYGp96lk+KOq5ahlOItm5pZMWfkg2dhXZDP3byeP3ndCt745Sf5+L27+fEHXoXLUJMa8wu/OMiZzjBv37qA165snHR/QmkhFn4JYTB1gh93V+If5fvWTjx7KN80BJ4AHmUSjoy/cOshiS7ktxhPiCDRMbn9U/n5fYHpE/zqgIc/e/2qMWI/nLlVfj77lnW8dLqbb/3mOCfaB3jqSPuEFnX7Y0m+/dRxfrHvAu/7zxd529eeIZ4cW/NAKF9E8EuIQrs7spH0VBEcJfgq2kNY+/B4fXn1ZTjplqNpcvOMxlVowfdVYChNZNTYOiX4welx6eTDzZc2c/0lc/iHXxzkdV/6P373289zw5ef5Omj+VXPeupIGwlT873f38bn37qe3We6+foTx4o0a2EmIoJfQri0iZ6CKB0A01dFiJHhjEain7AK5N2X4aRITpd9czRubUIBH2rKyfwZ7h8ZcWQ5+fmnetPVRFBK8YVbN/Lha1fwj2/fyFfetRmAd3/ree558XTO/Tx+sI1Kv5vty+r5ve2LefPGefzHY0c52jr+/4tQHojglxCF9m9nQ/uqqdARLHPIdeCK9xE28o9qMXx2wZR4ePwyhx6SaJc37zEy4XLCLqP9IxeglVNj1x+Y+RY+QEOFjz9/w2p+e+tCfmvjPH750deyem4l9+48m9P1WmseP9TKa1c14nHZf/afvWkdQZ+LO3+yN6cFdaH0EcEvIaZS8FWgGkNp+oYVAPckB4hOQPDdjksnPo6Fry0LjzLBVTiXzqDgD4zKTpkIE9Y+DNfUrIkUGr/HxevXzuWl0930RBLjtt9/vpfWvhivWz1UEa2hwscnb1zDjlNdPHawtZjTFWYIIvglhJskTNGireGUBxzoGarO5E32E8un+ImDywl9jEezW/jJpCNcBXyoeZxF2fioIigqGSaq8luLmGlcvboR09I8k4Mv//GDrSgF16weWfP4bZsXsKA2wH88flSs/FmACH4J4dIWuoDWbzY8ITsSJ9I7JPh+a4CEO38XiNdxm5jR7LHkyUTcflFAl05K8Efn9nclwkQnsB4xk7hsYQ2VfjdPHG4bt+1jh1rZuKCGhoqRDzmPy+CPrl7Oy6e7B3f9CuVLQQRfKXWDUuqQUuqoUurONOd9Sql7nPPPK6WWFGLc2YYLc8osfK8j+NG+IZdOwBog6cl/kXNI8LNb+PG4vRtXFfChlkoEl4yMtPBdZoSY8hdsnOnA7TJ4zYoGnjjcltU6P3Kxj11nurluTfoC9+/YsoCGCh9ffVwidsqdSQu+UsoFfAW4EVgL3KaUWjuq2R1Al9Z6BfCvwBcnO+5sxI05ZRa+v9IW/PjAkOAHJ1D8BMCXEvx4dsE3E4UXfJ9T13b0twuXGSFulLbgA1y9qpELPVGOZNmJ+8+/OkzI6+Z3ty9Oe97vcfGHVy3lqaPt7JVKXGVNISz8bcBRrfVxrXUc+CFw86g2NwPfc17fC1ynlJItfnkwuKA5RYu2gap6ABKO4FumSYgoOs/iJzCUX1/HxhH8lA+/gIIfSKV6HlUExWNGSLhK26UDth8f4IlD6d06u89088v9LfzhVcuoC2V2ld12xSL8HoO7M4R5pvsGobVm56kuqc9bQhRC8OcDZ4a9P+scS9tGa50EeoD60R0ppd6vlNqhlNrR1ja+X3I2YZpJ+8UUCX6o2v7vsSJ2wrT+vm4MpVG+/F06/pTgj2PhJx0L3yig4Acr7AeUHlW9y2tFSZaB4M+rDq8rG9AAACAASURBVLCmqZLvP3eK1t6hncxaa3rCCf7x4YPUhbzccdXSrP1U+T28acM8Hth1nkh8pIB/4t49vOc7L2BZQ6J/oSfCe//zRW792jN8+O6XMS1Z8C0FZtSirdb6Lq31Vq311sbGxvEvmEUUI4IlG5VVtVhaoZ38OWHHl2/kWfwEwJ1KUJbInu43tWir3AVctPX6iGkPxMcKvlkGgg/wd7dsoKM/xm3ffI5nj3XwsR/tZu2nH2bT3/yKp4928CfXLKfCN/7vze9sXUhfLMlDey8MHvvNkTbu2XGG3xxp5ycv2TH/O0528oZ/eZIXTnTylk3NPLz/In//0IGifb5yRGvNqY4BXjzZySOvXKSlJ7d6EZOlEOpxDlg47P0C51i6NmeVUm6gGpCQgDxIuTuUa2oE33C56FVDOfEjjuC7AhMoo+jykMCFSmS38K2U4Bd4nSKsAhijBN+no5ju8hD8LYtr+c/3beP277zAbd98joDHxVsva2Z5YwWL6oJcf8ncnPrZtrSOpQ0h7nnxDLduWUA8afHZ+/ezuD5ITdDLPz58iI0Lavij7++kodLHd993OYvrQ9SHvHz7qRPsONVFdcDD/Bo/ly2q5TUrGmiuKY97XChePt3FD54/zZOH22jtG5ky/LJFNbz3VUu4aVMzxfJ4F0I9XgRWKqWWYgv7O4F3jWpzP3A78CzwduAxLUG/eZFMpCz8qVm0BRggNJgTP9Zvu3Y8wfx9+AAR/Khk9kLmqW8xhruwnzGi/BjJkd8u/ESx3MGCjjOdbFtaxw/+8Ap2nuzi7VsWUJvFX58JpRS/vXUhX/zlQX7w/CmOXOznWNsA3759K3UhL7d89Rlu+o+n8LoNvnX7VhbX23sy/vrNa/F7XOw9Z28C23W6i7tfOIPPbfDvt13GG9c1Tfrz/XzPBc52hdm2tI5Kv5sdJ7sIx03e9+olRRPHQnKsrZ8vPXyIX+xrocrv5rWrGnn1igbm1wQI+dw8d7yDn+06x0d+uIt7d57lb9+6fvD+FpJJC77WOqmU+lPgYcAFfEdrvV8p9TfADq31/cC3ge8rpY4CndgPBSEPzGTK+p26jNbDc+LHBmzB91ZMrFB6TPkxEtkFfzBKx13YDVExFcQ96ttFQMfQnvIRfIDNi2rZvCjPTKajuHXLfP7t14f51H37AHj92rlc53xDeNvm+fz05XN88z1bWd44FK3lMhR33jhUKtKyNEda+/nET/bwgf/eySduWMPlS2qpCXpHXJcr/bEkH/vxLqKJsZk9K3xufvvyhWmumhm09ET5f48d4YcvnsHvNvjz16/ijtcsJTTKxbZlcS0fuHo5//3cKf7p4UPc8b0d/Oqjr8UocPrqgqiH1voh4KFRxz497HUUeEchxpqtDEawTKGFPzwnfjJsu3YCeZY3TBFTflxmdsG3nIdaIRdtAWJGALc5ZOHHY1G8ygRveQl+IZhT6efZO6+jMxzHtPQIgf7C2zby4WtXsqQhu+VpGIrVTZXc/Yfb+dDdL/OFXxwcPPeZt6zlfa/OvoA8ml/tbyGasPj6724maWnCcZPNi2r51H17+fzPX+Hq1Y3MrZo5Ibampfn2U8f5+d4W9pztxqUUv7d9MX967YoxG9+G4zIUt79qCW9c10RLb7TgYg9SAKVkSAm+MYUWftxdSVXMXsAbFPyq/KpdpUgYPtzjCL5ZJJdO3B3Elxzy4UcG+vACyjs95Q1nOrUhb1qXkNdtjCv2wwl4XXzj97aw60w3fdEE337qBF/85UGuWT2HpXn0c9/L51hQG+ANa5tGiOAXbt3IDV9+kr/66T7u+r0tRXHtPLD7PP/6yGE+dN0KbrlswbjtLUvzl/+7l3t2nOHShTX8+fWruPnS+Syqz924aKr201RdnAfYjIrSETJjJqc2LBPsnPghJye+dnz5eVe7cogbATzjWvgpwS9clA5A0hXCZw1Z+FEnr44hgl90XIZiy+Jarlk9hy+9YxNel8HH7909IsTzwIVefn3gYtpY/9a+KE8fbeetl84fY/EubQjxsTes4pFXLvKpn+4bLOZy5GIfvdHsCeVMS/PUkXb+6qd7+ez9+7n7hdMjisaH40k+fu9uPnT3y7T1xfize3Zz15PHsu5otizNX/9sH/fsOMOHr1vJTz/4aj503cq8xL7YiIVfIpjJlH976lw6pq+KkHYEP9qLpRWhivzDMgGSrgCeePZsmZbzGV0FFnzTHcRvDYW9xZxiKKnCLMLUMLfKz6ffso6/+PFu7vjei/zO5Yt44UQn333mBJaGN66by9/fsoH6YW6PB3ZfwNLw1sua0/b5B69ZRudAgq8/cYz953qIm5oDF3rZvKiGH/3RlbhdI23avmiCHzx/mv98+gQXe2OEvC40EI6bBDwu/urNl7BpQQ0f/uHLnGgf4EPXruCPr1nOx+/dw98/dJD7d5/n6lWNrGuupiboobk6wOL6IG19MT7249385kg7H7h6OX92/cpi3soJI4JfIljOxis1hT587aumUkUwk0lUrI9+FaDKmNiXwqQrQKWVPQVvyofv8hRW8C1PiABD1ltK8F0i+FPOrZvnc7ozzA+eO8Xjh3aiFLxr2yIW1Ab510cOc8O//Yaff+g1zHF88j/bdY7186tYMSf9hj/DWTBeP7+KT923j0V1QX5v+2K+/9wpvvHkcT74uhWDbR955SIf+9EueqNJXrOigc+8ZR3XrpmD12VwvH2Azz2wn0/dtw+lYE6ljx/8wRW8ankDAP/+zsu4bFEtv9h7ga8/cXzERrO6kBfT0sSSJn93y3retW3RjI0cEsEvEUwnRr3Q/u1sKGeTVX9vl138hBATC8oEyx3Am6aY+HC0aX8NdxX4M1re0IhC5omI/a3FM431bGcrSin+/PWr+NC1K3jueAf1IR9rm+3fqqtWNvC2rz3D3zz4Cv/xrs3c9/JZ9pzt4XM3rRu33zdvbOa3NsxDKYXWms5wnC8/epjXrmxkw4JqfrTjDHf+ZA8b5lfz+beuZ+OCka7JFXMq+N77tvFfz57kwIU+PnHjmhGpKAxDccdrlnLHa5bSF01wrjtC10CCE+0DvHS6i55Igk/csDrjg2mmIIJfIgxa+FO4aDs8J7433klkAsVPUpjuIAEdy9rGSgm+p7BhmcpbiVeZxKJhfP4gCSeRmrtEql2VIx6XwVUrR+6mXz+/mj993Qr+5ZHDXLH0JF/85SEuX1LLu69YlFOfKataKcXf3rze3gn8H08R8roYiJtctbKBr//uljEhkSkMQ/HeHCKIKv0e1jTZRsmVy+t5V47zmwmI4JcIpiOGxhQu2qZy4vd3tbAsvJcDddeRX0Dd8M6C+Iihtc74dVenXDoFjsPHcd1E+nvx+YODmTN9Ivgzjj+6ehk/3XWOv/7Zfqr8br78zsvG+OFzoTbk5Z73b7fTFvRGqQ54+JNrVuB1z+44FRH8EsEqUshiNlI58Tt2PcQqFcG9+vUT7kt7AgSJEUuY+L3pf+0GXTqewn5GwylzGO7voaahCdPJ2ukNTNRBJRQLn9vFP9yygfd/fydfvHUD8yeRmmFZYwV/dLU81Icjgl8ipNwdhc4zk41UTvzmsw+R1AbLr3jzhPtS3hCG0kQjA/i96SN9UoLvLvCircvJ8JnaLWw5gu8LihjMRK5YVs9Lf/16XEXYeDTbmd3fb0oInSzOgmY2gk6K5MXWGY54L6G6tmHinTm7WqPhzKGZKZeOp8A+/NTibCo6J5WmORCa2QtssxkR++Iggl8ipBZtp9KlE6oeEvju+a+dVF+pEMhYOEtdWyvl0imshe91Er4lnA1XOm6HaAaCIvjC7EIEv0TQKcGfwiidisoaLG1bWg2X/tak+jJ8tvskHsli4RfJpZPK8JmM2mOrRJiI9mK4pqY+sCDMFMSHXyJYZiqCZeosfDsnfpAkLpZveNWk+nI7Fn48ksXCdx5qhXbpDBUyTwn+AFHlRzK1C7MNsfBLhCELf+oEH6DV1cSRumsmbQ2nYt4T0WyCH8fUCpe7sHaI3/HVW06ZQyMZIUqBQz8FoQQQC79EGNqFWlh3x3jM+dAjLPBNPnOfxylzmIxmqXplJUjiptCOlpCT8M1yXDquZISYMXPS6QrCVCGCXyJMx6ItQFXNmFrzE8LrWPipGPh0KCtJElfBbW+fP4ip1WBdW7cZIW6IQ0eYfYhLp1Qo0oLmVJGKebeyCb6ZIKEKb4Mow2BABVBOOKbbjJAQC1+YhYjglwjamvoonULiC6T86OO7dIpBhACGU+bQa0VIusTCF2YfIvglQmrR1j3FLp1CMbjJKZ7dpWMW3INvEzUCuJOO4OuoCL4wKxHBLxWKtClpqvCkcs8nwhnbGFYCswguHYCYEcSdtMf2WVFM98ypQiQIU4UIfqlQ4hY+hkEYHyqL4CsrQbJIgp8wAnhM28L3E0V7RPCF2YcIfomgrdJetAWI4kMls1j4OolZJB9+3B3Ca9k1dQM6hiWCL8xCRPBLBcsEwFWii7YAMeXHSGaueqWsZNFcOnZd2zCJeAyvSoIIvjALEcEvFawkplYlnf8lrvy4xrPwiyX4nhB+HSU84KRX8Eo9W2H2IYJfKpjJooUsThVxw4/HjGQ877ISWEUSfO0OEtLhwRTJIvjCbEQEv0RQVgKzxP+7Eq4Abiuz4Bs6WTTBN5rW4VcJTjx1DzCUrlkQZhOlrSCzCStZtAiWqSLp8uO1MvvwDZ3ELFLN3k033kErdSw/8DUAXH6pdiXMPkTwSwSlzaJtSpoqkq5gVsF36SSWKk7Yqc8f5PiqO2jALnPo9ouFL8w+RPBLBStZ8i4dyx3Ar7MLvi6ShQ+w6eaP0IldDMUjFr4wCyltBZlF2JkkS9ulY7qD2QWf4vnwwU7vcGjpe+zX1Y1FG0cQZiqlrSCzCKWTWKq0XTq4AwSIZTxtW/jF3Ul8+bs+w77ntrF+9aVFHUcQZiJi4ZcIRhETi00Z3iBelSQej6c97dZJrCILvtvjZf1VNxd1DEGYqYjglwhKJzFL3cJ3drdGBtKXOXRhFtWHLwiznUkJvlKqTin1iFLqiPNvbYZ2plJql/Nz/2TGnK0oy8QqcQtf+eyF0tTmp9G4SYIIviAUjcla+HcCv9ZarwR+7bxPR0Rrfanzc9Mkx5yVFDPtwFRh+GwLPxruTXverU20q3STwwnCTGeygn8z8D3n9feAt06yPyEDSpslv2jrciz8eCS9S8dDccMyBWG2M1nBn6u1vuC8bgHmZmjnV0rtUEo9p5TK+FBQSr3fabejra1tklMrL4qZdmCqcDvpDBLR9IJvu3RKNN+/IJQA4yqIUupRoCnNqU8Nf6O11kopnaGbxVrrc0qpZcBjSqm9Wutjoxtpre8C7gLYunVrpr5mJUYZWPjugG3hJ9JY+JZp4lIaXCL4glAsxhV8rfX1mc4ppS4qpeZprS8opeYBrRn6OOf8e1wp9X/AZcAYwRcyY1hJEiVeh9XjpDNofvyj9D7+QQ5u+Djbbv0oAIlEDB+I4AtCEZmsS+d+4Hbn9e3Az0Y3UErVKqV8zusG4NXAK5Mcd9ZhUPoW/oKVl7Kz8lpOVW4mTIDqA3cPnkvE7Q1ZSgRfEIrGZAX/C8DrlVJHgOud9yiltiqlvuW0uQTYoZTaDTwOfEFrLYKfJ4Yu/Rh1f7CCLR+7jy0fu48Ti9/OysQh2ltOA2AmnM1Y4sMXhKIxKcHXWndora/TWq/UWl+vte50ju/QWv+B8/oZrfUGrfUm599vF2Lisw1XGSzaDmfu5W/DUJrjT/8EgIQj+MotYZmCUCxkp22JYGCiS9ylM5ylay/nvJqD9+gvAUgmxKUjCMVGBL9EKHbq4KlGGQanG65mTXgn4f4ezETCPi6CLwhFQwS/RHBpE11GLh2Aik034VcJDj59P2ZSLHxBKDYi+CWCnVisfFw6AKu3vZFeQiQPPISZtC18Q3z4glA0RPBLBFvwy8v69Xh9nPKtorr/OKb48AWh6IjglwguTCijRdsUUf9capJtg2GZYuELQvEQwS8R7EyS5Wf9JivmUa+7SMbt0oeG2zfNMxKE8kUEv0QoVwvfqJ6PW1lE2u0NWIa7/B5qgjBTEMEvEcq1GpSvbgEAiY4TALhE8AWhaIjglwDasvAqsywTi1U0LgLA1ZOy8MWHLwjFQgS/BLAsy35RhhZ+bdNiAELhcwC4POLDF4RiIYJfAiSckMWyFPyGecS1m7r4eQDc4tIRhKIhgl8CpDYlKVf5Cb7hctFu1NOoO+z3HnHpCEKxEMEvAZLJpP2izDZepehxN+BWttvKJT58QSgaIvglwNAu1PKz8AHC/qFSyB6v+PAFoViI4JcA1qCFX56CnwgOCb6EZQpC8RDBLwGSSSftQBmGZQJQPX/wpUTpCELxEMEvAcwyt/C9tQuGXotLRxCKhgh+CWCZqfJ/5WnhBxsWDr52S5SOIBQNEfwSYDAss0yjdGqalgy+ljh8QSgeIvhF4NCOx9j9xTfQdv5kQfpLuXTKNUqnfu5CLK1IaBfKkF9JQSgW8tdVYPb95mcsfOCdbIo8z/GnflyQPq3Uom2ZWr8er48OVUOC8nygCcJMQQS/gBx+6QlWPfr7XHQ10UUlxrkdBenXNJ3yf2W6aAvQ5W4kWYbpnwVhJiGCX0A69vwCr0pS88cPcyq4njl9+ybUz/P/8T6e/fZfYJkmANZgvdfytPABBryNmGLhC0JREcEvIEZ/Kz2EqG2cR3TOZhZbZ+npbMurj0Q8xpa2n3LlmW+y619vIRruxzJTuXTKV/Dji6/mROjS6Z6GIJQ1IvgFxBNpo9uoA6Bi+RUAnN77m7z6uHj6CG5lsd+7ic39T7Dv67ejTXvR1lWmi7YAV/zOJ9j8/z0w3dMQhLJGBL+ABOLt9LttwV+88Sosreg/9lxefXSePQSAuvYv2VF1PYt6Xxpy6UiMuiAIk0AEv4BUJjuJ+Brs19V1nHItItj6Ul59RC4eBaBx0RqSdauZQyeJgS4AjDK28AVBKD4i+OPQ09XOCz/5MjpVdSoLdVYXyeCcwfdt1etZHD2Q07UpdOcJItpLQ9MiPHNWApC4eBCQxGKCIEwOEfxxeOXHf8O2vZ/hwAu/ytpuoK+boIpBxZDgs+Byaujn7PH9OY/n6zvFRVcTyjCoWXiJfazrCFDGydMEQZgSRPCzYJkmy84/CEDvrvuztu28eAYAV1XT4LHGNa8CoGV/7gu3NdFzdPntZGLzltqCXx85AZR3WKYgCMVHBH8Uz9/zBZ77788A8MozDzKXDvp1gObW/8t6XV+7XYTbXztv8Nii1VsIax/muZdzGltbFnPNFmKViwAIVlTTSh3N5gVAqkEJgjA5JiX4Sql3KKX2K6UspdTWLO1uUEodUkodVUrdOZkxi03zwe+x/eiX2fvkz4js+AG9BNm34v0sss5x5sjujNdFOm1RrqhvHjzmcrs57VlGVfeBjNf1dFzk5Ye/h2WadLScIahiqLqlg+fbvAvxKHOwP0EQhIkyWQt/H/A24MlMDZRSLuArwI3AWuA2pdTaSY5bFBLxGE3WRQDmPPZnrOv+Pw7WXcfi174bgHPP35f52h5b8KsbF4w43lNzCQtjRwd3zaaIx6I89/1Po/7fZVz27IfZ/eu7aTtjL84G5qwYbNdfuWTwtVj4giBMhkkJvtb6gNb60DjNtgFHtdbHtdZx4IfAzZMZt5C8/Kv/JhoZAODCyQN4lMnzjW+nXncRVDGqrngP8xav5rixhMrTj2bsR/dfJKFd1NQ3jTiu5m2kUkU4f2Kklf/ST77E9mP/xonAOrqpwNx3H33nDwNQu3D1UL+1ywZfS+pgQRAmw1T48OcDZ4a9P+scG4NS6v1KqR1KqR1tbfmlJJgI547v57JnPsjuB74CQMdJO/dNzRXvZseqj7LHv5XVl18PwMXma1kd209Px8W0fbkGWulS1RiukQnA6lZcbl9/+MURx43W/bRTw6ZPPMLh2mu4pOcprIsHMLVi7sKVg+38TUPi75KNV4IgTIJxBV8p9ahSal+an4Jb6Vrru7TWW7XWWxsbGwvd/Rh6Wu3nkDpvL6rGWuwvK03LN7L93Z9h452/HszPXrvxTbiVxfGdj6Ttyxdto8dVN+b4wtWbSWgX8bMjF25D4XN0uO1vA4HL3k5IRVnd8gAXjUa8Pv9gu7pFlwy+FgtfEITJMO4qoNb6+kmOcQ5YOOz9AufYtBPtbgGgsfcVAIzOo7RTQ0Ntw5i2NfPshdREb2vavkKJTga8Y6/z+YMccy8i1PnKiOO1iRbOV6wH4JIrf4uuxyqppZdz3mU0D2vXtHgNSW3gVpZsvBIEYVJMhUvnRWClUmqpUsoLvBPIHtQ+RSR6bbfRQvM00XA/VQMnafUuTNu2um4uAOZAR/rzZicx/1jBB+isXMP86OHBHbfJRJw5VhuJKnsst8fLkbprABgILRpxrdfnp8WwN3OJhS8IwmSYbFjmLUqps8CVwM+VUg87x5uVUg8BaK2TwJ8CDwMHgB9prXPfelpErH5b8N3K4uQrzzM3cYb+iiVp2wZClUS1BxXpGnPOTCap1T2Yw9IqjDg/dyP19NDechqA1nMnbIu9bmiswKW3AqBrxo7f4VuIqdWY9QFBEIR8mGyUzn1a6wVaa5/Weq7W+o3O8fNa6zcNa/eQ1nqV1nq51vrvJjvpQmGE20hq+xZ07XmYWvqw6ldmbN+rKjGiYwW/u6MFt7IwKuemva562RYAzh+0M2d2nrOjcYJzhiJw1r7qLTy7+AMsed3tY66PVK8gjH/McUEQhHyY1TttPdEOLhhNdFLF/LN2CoXAvNUZ2/cbVXji3WOOd7eetfurmTfmHMDCS7ZhaUX4lL1wG7l4HIDa+asG27jcbq583xdpWrhizPVr3vFZLtz8wxw/lSAIQnpm9dZNX7yLPncdna75bIraYZP1i9ZnbB9xV+NPjBX8gQ57DTpY2zzmHEBFVS2nXc0E2uydumbnKUytmLNgWdr2o6lpaKKmoWn8hoIgCFmY1RZ+KNlF1FtLuGEDAHHtpmnxqoztY95qgmbv2OPd9i7bysa02wsAaKnaxJLwXizTxN17mlbVgMfrm+QnEARByJ1ZLfjVVg8Jfz3+RY6P3TUPd5bNTQlfLZXWWMFP9tqbsWqyCD6Lr6SGfk4ffplQ5BydXrHYBUGYWmat4JvJJNW6DyvYwLxLtgPQFVic9RrLX0uV7h9T0ET1X2RA+wlV1mS8tnnDtQBc3Ps49YkWwsEFGdsKgiAUg1kr+N0dLRhKY1Q0Mnf+Mg561hJf8rqs16hgHW5l0dvTOeK4d+ACncbYXbbDmb9sLe3U4Dn1JHPoJFm1KGt7QRCEQjMrFm0P7vg1iXAvG157y+Cx3o4L1APuykaUYbDmU8+O248rVA9Af9dFqoftxq2KnqPLN5/0W7ZslGFwumIT6/qeAQXu+iUT/DSCIAgTY1ZY+Prhv6b5sY+MSFE84OSv91fn7kv3VNoiP9A9MrHbHLOFSEU2ubeJz78Cn0oAUNG0POdxBUEQCkHZC762LJoTJ6mnh2N7nxk8Hu2xF1or6nIXfH+1ndAt0jMk+D2dbVQxgK4Z30XTsPaawdf1CzJHAwmCIBSDshf8jtazVGPnu29/+cHB40knj05lfe6CH6qxUyck+toHj7WdsTNs+hrHj6lfuu4K+nSAmPbQ0CQ+fEEQppayF/yWI07qY+2h9vxQYS490Iap1WBStFyoqrUFP9k/lECt98JR+9y8zCkZUrjcbo4FN3He1Sx5cQRBmHLKftG2/6ydp213/Q1s6XiQno6LVNfPxQi3062qqM+jTmxlTQOmVhAeitKJt9lpEhoXZU7JMJwFv3cX0XBfHp9AEAShMJS9ha/aD9JDiKor34tLaY4+9wAA3mgHfUZ1Xn0ZLhe9qgI1LIGa6j5FNxVU1dTn1EdD82IWrMicvkEQBKFYlL3gV/Ye47xnCSsvu4ZuKrAO2xWr/IkuBty1effXrypxx4YEPzBwljZ3+qRpgiAIM4myFvxUhE5v5XLbf165jWU9z5JMxKlIdhP1Zd8slY4BVzXeYRkza2Pn6fOnT5omCIIwkyhrwe9oPUcN/ejGNQAYG26lnh72Pv4jqnU3yQkIftRTTSBp59Mxk0nmWq3EKrOnZBAEQZgJlLXgtxy1I3RC822f+YbX/TYXqce74+tUEcYK5V8oPeGtocLJmNl24SRelcSoE8EXBGHmU9aCP+BE6MxbcSlg1449vvgdrIvvBcCoyF/wTX8tVdoW/M6zRwAIzpVds4IgzHzKWvBpO0gvIeqbhtIerLzxg8S1HQPvqco9Bn+QQB1BFSMWDdPfYsfg18muWUEQSoCyFvzKvmOc8yxGGUMfs6FpEXurrgYgUJO+6Hg2VMj2+/d2tmJ1nHAqV40tSygIgjDTKFvB3//MQ6yIvUJvzdox52re8AkOeNbSvGpL3v16KoYyZkrlKkEQSomy3Gl76uBLLPzVH3DB1cya274w5vzyDdthw/jpkNPhrUplzGylvv8Ind55SBS+IAilQNlZ+O0tZ/Dc8zskcON5z0+orst/YTYbwWrbDWQ88UWWWicZWH1rQfsXBEEoFmUn+B6vnzb/Ejpu+j7NS3LLb5MPFU4CtbXxvbwcfDWX3/Lhgo8hCIJQDMrOpVNd18imTzxStP6r6mzBb6OWJe/79ogFYUEQhJlM2Ql+sfEHQjy7+APUbXgjqxvFey8IQukggj8BrnzfF6d7CoIgCHkj/ghBEIRZggi+IAjCLEEEXxAEYZYggi8IgjBLEMEXBEGYJYjgC4IgzBJE8AVBEGYJIviCIAizBKW1nu45pEUp1QacmkQXDUB7gaZTbEppriDzLTYy3+JRSnOFic13sdY6bdbIGSv4k0UptUNrvXW655ELpTRXkPkWG5lv8SiluULh5ysuHUEQhFmCCL4gCMIsoZwF/67pnkAejt8svgAABMFJREFUlNJcQeZbbGS+xaOU5goFnm/Z+vAFQRCEkZSzhS8IgiAMQwRfEARhllB2gq+UukEpdUgpdVQpded0z2c0SqmFSqnHlVKvKKX2K6U+4hyvU0o9opQ64vxbO91zTaGUcimlXlZKPei8X6qUet65x/copbzTPccUSqkapdS9SqmDSqkDSqkrZ/i9/TPn92CfUupupZR/Jt1fpdR3lFKtSql9w46lvZ/K5t+dee9RSm2eIfP9J+f3YY9S6j6lVM2wc5905ntIKfXGmTDfYec+ppTSSqkG5/2k729ZCb5SygV8BbgRWAvcppRaO72zGkMS+JjWei2wHfigM8c7gV9rrVcCv3bezxQ+AhwY9v6LwL9qrVcAXcAd0zKr9Pwb8Eut9RpgE/a8Z+S9VUrNBz4MbNVarwdcwDuZWff3u8ANo45lup83Aiudn/cDX5uiOQ7nu4yd7yPAeq31RuAw8EkA5+/uncA655qvOhoylXyXsfNFKbUQeANwetjhyd9frXXZ/ABXAg8Pe/9J4JPTPa9x5vwz4PXAIWCec2wecGi65+bMZQH2H/W1wIOAwt755053z6d5rtXACZxghGHHZ+q9nQ+cAeqwy40+CLxxpt1fYAmwb7z7CXwDuC1du+mc76hztwA/cF6P0AfgYeDKmTBf4F5sg+Uk0FCo+1tWFj5Df0ApzjrHZiRKqSXAZcDzwFyt9QXnVAswd5qmNZovAx8HLOd9PdCttU4672fSPV4KtAH/6bigvqWUCjFD763W+hzwJWwr7gLQA+xk5t7fFJnuZyn8/f0+8Avn9Yycr1LqZuCc1nr3qFOTnm+5CX7JoJSqAH4CfFRr3Tv8nLYf39MeL6uUejPQqrXeOd1zyRE3sBn4mtb6MmCAUe6bmXJvARzf983YD6pmIESar/czmZl0P8dDKfUpbJfqD6Z7LplQSgWBvwQ+XYz+y03wzwELh71f4BybUSilPNhi/wOt9f86hy8qpeY55+cBrdM1v2G8GrhJKXUS+CG2W+ffgBqllNtpM5Pu8VngrNb6eef9vdgPgJl4bwGuB05ordu01gngf7Hv+Uy9vyky3c8Z+/enlHov8Gbg3c5DCmbmfJdjGwC7nb+7BcBLSqkmCjDfchP8F4GVTpSDF3tB5v5pntMIlFIK+DZwQGv9L8NO3Q/c7ry+Hdu3P61orT+ptV6gtV6CfS8f01q/G3gceLvTbEbMFUBr3QKcUUqtdg5dB7zCDLy3DqeB7UqpoPN7kZrvjLy/w8h0P+8H3uNEk2wHeoa5fqYNpdQN2G7Jm7TW4WGn7gfeqZTyKaWWYi+GvjAdc0yhtd6rtZ6jtV7i/N2dBTY7v9uTv79TvUAxBQsgb8JeiT8GfGq655Nmfq/B/gq8B9jl/LwJ2zf+a+AI8ChQN91zHTXva4AHndfLsP8wjgI/BnzTPb9h87wU2OHc358CtTP53gKfAw4C+4DvA76ZdH+Bu7HXFxKO+NyR6X5iL+h/xfnb24sdfTQT5nsU2/ed+nv7+rD2n3Lmewi4cSbMd9T5kwwt2k76/kpqBUEQhFlCubl0BEEQhAyI4AuCIMwSRPAFQRBmCSL4giAIswQRfEEQhFmCCL4gCMIsQQRfEARhlvD/A0C94t6HQJb3AAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df4[\"BGSM1 Predictions\"].plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "tljAvKrfFGsA",
        "outputId": "987e80a0-eac4-4d0a-8ff2-e84056b0f85e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f815f50e4d0>"
            ]
          },
          "metadata": {},
          "execution_count": 173
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = pd.DataFrame(data={'BGSM1 Predictions': BGSM1_preds,\n",
        "                          'BGSM1 Actuals':BGSM1_actuals,\n",
        "                          'BGSM2 Predictions': BGSM2_preds,\n",
        "                          'Pressure Actuals': BGSM2_actuals\n",
        "                          })"
      ],
      "metadata": {
        "id": "X22PQrv4FNX-"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}