facebook_prophet_final.ipynb

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      "name": "Untitled6.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOA4Bqe8gZ5ojdV6JuSrBSg",
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      "name": "python"
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/rohithmn01/prom_forecast/blob/main/facebook_prophet_final.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
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          "base_uri": "https://localhost:8080/"
        },
        "id": "HEsEUXRR2tTP",
        "outputId": "dadbd6ff-4c7f-4d85-d3c9-bc157993f06e"
      },
      "source": [
        "import requests\n",
        "import copy \n",
        "import pandas as pd\n",
        "URL = \"http://34.67.240.237:30000/api/v1/query?query=node_memory_MemFree_bytes{instance=\\\"10.56.0.2:9100\\\",job=\\\"node-exporter\\\"}[1d]\" \n",
        "r = requests.get(url = URL) \n",
        "\n",
        "data = r.json()\n",
        "\n",
        "data_dict={}\n",
        "metric_list = []\n",
        "\n",
        "for i in data['data']['result']:\n",
        "    counter=0\n",
        "    for j in i['values']:\n",
        "        data_dict = copy.deepcopy(i['metric'])\n",
        "        data_dict['time'] = j[0]\n",
        "        data_dict['value'] = j[1]\n",
        "        metric_list.append(data_dict)        \n",
        "  \n",
        "df_metric = pd.DataFrame(metric_list)\n",
        "\n",
        "df1 = df_metric[['time', 'value']]\n",
        "df1['time'] = pd.to_datetime(df1['time'],unit='s')\n",
        "df1 = df1.set_index('time')\n",
        "df1[\"value\"] = df1.values.astype(float)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:23: SettingWithCopyWarning:\n",
            "\n",
            "\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",
            "\n"
          ],
          "name": "stderr"
        }
      ]
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        "id": "BsFYPV2n20Ew",
        "outputId": "9c9b993b-628c-41bb-f025-65cf612711dd"
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      "source": [
        "df1"
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      "execution_count": 10,
      "outputs": [
        {
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              "<p>17280 rows × 1 columns</p>\n",
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              "                                      value\n",
              "time                                       \n",
              "2021-08-29 11:45:12.460000038  2.615091e+09\n",
              "2021-08-29 11:45:17.460000038  2.614931e+09\n",
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              "2021-08-29 11:45:32.460000038  2.616164e+09\n",
              "...                                     ...\n",
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              "2021-08-30 11:45:07.460000038  2.586538e+09\n",
              "\n",
              "[17280 rows x 1 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
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      "cell_type": "code",
      "metadata": {
        "id": "kiNWQcLx4TFN"
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      "source": [
        "df1=df1.reset_index()"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4yGvxiIz4cJB"
      },
      "source": [
        "df1 = df1.rename(columns={'value': 'y', 'time':'ds'})"
      ],
      "execution_count": 17,
      "outputs": []
    },
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      "cell_type": "code",
      "metadata": {
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          "height": 204
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        "outputId": "6dbf7a1f-de1a-4ab6-9cf6-f3389db0d60b"
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      "source": [
        "df1.head()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "                             ds             y\n",
              "0 2021-08-29 11:45:12.460000038  2.615091e+09\n",
              "1 2021-08-29 11:45:17.460000038  2.614931e+09\n",
              "2 2021-08-29 11:45:22.460000038  2.615648e+09\n",
              "3 2021-08-29 11:45:27.460000038  2.615190e+09\n",
              "4 2021-08-29 11:45:32.460000038  2.616164e+09"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "onELjnIQ481V"
      },
      "source": [
        "from fbprophet import Prophet\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.metrics import mean_squared_error"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hRALuvda3c70"
      },
      "source": [
        "df1['y_orig'] = df1['y'] # to save a copy of the original data..we'll see why shortly. \n",
        "df1['y'] = np.log(df1['y'])"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "8Imn_Rvw4_bX",
        "outputId": "4850f190-fb47-41f8-8763-f26a83bc9b50"
      },
      "source": [
        "df1.tail()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>y_orig</th>\n",
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              "      <th>17275</th>\n",
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              "      <th>17277</th>\n",
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              "    <tr>\n",
              "      <th>17278</th>\n",
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              "      <td>2.586968e+09</td>\n",
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              "    <tr>\n",
              "      <th>17279</th>\n",
              "      <td>2021-08-30 11:45:07.460000038</td>\n",
              "      <td>21.673586</td>\n",
              "      <td>2.586538e+09</td>\n",
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            "text/plain": [
              "                                 ds          y        y_orig\n",
              "17275 2021-08-30 11:44:47.460000038  21.673786  2.587054e+09\n",
              "17276 2021-08-30 11:44:52.460000038  21.673851  2.587222e+09\n",
              "17277 2021-08-30 11:44:57.460000038  21.672710  2.584273e+09\n",
              "17278 2021-08-30 11:45:02.460000038  21.673752  2.586968e+09\n",
              "17279 2021-08-30 11:45:07.460000038  21.673586  2.586538e+09"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZtkELoKK5Xxs",
        "outputId": "80917c55-bfd8-4ca1-cf0e-2602086afc75"
      },
      "source": [
        "model = Prophet() #instantiate Prophet\n",
        "model.fit(df1)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:numexpr.utils:NumExpr defaulting to 2 threads.\n",
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<fbprophet.forecaster.Prophet at 0x7efedeb64810>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "id": "A2vzWOXRB5HT",
        "outputId": "03a5c935-7a4f-4b5c-87c1-3aeaaf502683"
      },
      "source": [
        "'''     'year': 'A',\n",
        "        'quarter': 'Q',\n",
        "        'month': 'M',\n",
        "        'day': 'D',\n",
        "        'hour': 'H',\n",
        "        'minute': 'T',\n",
        "        'second': 'S',\n",
        "        'millisecond': 'L',\n",
        "        'microsecond': 'U',\n",
        "        'nanosecond': 'N'}\n",
        "'''\n",
        "\n",
        "#future_data = model.make_future_dataframe(periods=6, freq = 'H')\n",
        "future_data = model.make_future_dataframe(periods=10, freq = 'H')\n",
        "future_data.tail(15)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>ds</th>\n",
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              "  <tbody>\n",
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              "      <th>17275</th>\n",
              "      <td>2021-08-30 11:44:47.460000038</td>\n",
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              "      <th>17276</th>\n",
              "      <td>2021-08-30 11:44:52.460000038</td>\n",
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              "      <th>17277</th>\n",
              "      <td>2021-08-30 11:44:57.460000038</td>\n",
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              "      <th>17278</th>\n",
              "      <td>2021-08-30 11:45:02.460000038</td>\n",
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              "    <tr>\n",
              "      <th>17279</th>\n",
              "      <td>2021-08-30 11:45:07.460000038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17280</th>\n",
              "      <td>2021-08-30 12:45:07.460000038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17281</th>\n",
              "      <td>2021-08-30 13:45:07.460000038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17282</th>\n",
              "      <td>2021-08-30 14:45:07.460000038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17283</th>\n",
              "      <td>2021-08-30 15:45:07.460000038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17284</th>\n",
              "      <td>2021-08-30 16:45:07.460000038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17285</th>\n",
              "      <td>2021-08-30 17:45:07.460000038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17286</th>\n",
              "      <td>2021-08-30 18:45:07.460000038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17287</th>\n",
              "      <td>2021-08-30 19:45:07.460000038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17288</th>\n",
              "      <td>2021-08-30 20:45:07.460000038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17289</th>\n",
              "      <td>2021-08-30 21:45:07.460000038</td>\n",
              "    </tr>\n",
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            "text/plain": [
              "                                 ds\n",
              "17275 2021-08-30 11:44:47.460000038\n",
              "17276 2021-08-30 11:44:52.460000038\n",
              "17277 2021-08-30 11:44:57.460000038\n",
              "17278 2021-08-30 11:45:02.460000038\n",
              "17279 2021-08-30 11:45:07.460000038\n",
              "17280 2021-08-30 12:45:07.460000038\n",
              "17281 2021-08-30 13:45:07.460000038\n",
              "17282 2021-08-30 14:45:07.460000038\n",
              "17283 2021-08-30 15:45:07.460000038\n",
              "17284 2021-08-30 16:45:07.460000038\n",
              "17285 2021-08-30 17:45:07.460000038\n",
              "17286 2021-08-30 18:45:07.460000038\n",
              "17287 2021-08-30 19:45:07.460000038\n",
              "17288 2021-08-30 20:45:07.460000038\n",
              "17289 2021-08-30 21:45:07.460000038"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "id": "nWeqlmKTCJPI",
        "outputId": "89734e23-27f8-46e3-9409-77acadc9cd7c"
      },
      "source": [
        "forecast_data = model.predict(future_data)\n",
        "forecast_data[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail(15)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ds</th>\n",
              "      <th>yhat</th>\n",
              "      <th>yhat_lower</th>\n",
              "      <th>yhat_upper</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>17275</th>\n",
              "      <td>2021-08-30 11:44:47.460000038</td>\n",
              "      <td>21.673464</td>\n",
              "      <td>21.672985</td>\n",
              "      <td>21.673980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17276</th>\n",
              "      <td>2021-08-30 11:44:52.460000038</td>\n",
              "      <td>21.673463</td>\n",
              "      <td>21.672982</td>\n",
              "      <td>21.673949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17277</th>\n",
              "      <td>2021-08-30 11:44:57.460000038</td>\n",
              "      <td>21.673463</td>\n",
              "      <td>21.672991</td>\n",
              "      <td>21.673951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17278</th>\n",
              "      <td>2021-08-30 11:45:02.460000038</td>\n",
              "      <td>21.673462</td>\n",
              "      <td>21.672974</td>\n",
              "      <td>21.673938</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17279</th>\n",
              "      <td>2021-08-30 11:45:07.460000038</td>\n",
              "      <td>21.673462</td>\n",
              "      <td>21.672976</td>\n",
              "      <td>21.673946</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17280</th>\n",
              "      <td>2021-08-30 12:45:07.460000038</td>\n",
              "      <td>21.673058</td>\n",
              "      <td>21.672575</td>\n",
              "      <td>21.673570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17281</th>\n",
              "      <td>2021-08-30 13:45:07.460000038</td>\n",
              "      <td>21.672655</td>\n",
              "      <td>21.672091</td>\n",
              "      <td>21.673237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17282</th>\n",
              "      <td>2021-08-30 14:45:07.460000038</td>\n",
              "      <td>21.672251</td>\n",
              "      <td>21.671528</td>\n",
              "      <td>21.672998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17283</th>\n",
              "      <td>2021-08-30 15:45:07.460000038</td>\n",
              "      <td>21.671848</td>\n",
              "      <td>21.670893</td>\n",
              "      <td>21.672894</td>\n",
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              "    <tr>\n",
              "      <th>17284</th>\n",
              "      <td>2021-08-30 16:45:07.460000038</td>\n",
              "      <td>21.671444</td>\n",
              "      <td>21.670207</td>\n",
              "      <td>21.672723</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17285</th>\n",
              "      <td>2021-08-30 17:45:07.460000038</td>\n",
              "      <td>21.671040</td>\n",
              "      <td>21.669440</td>\n",
              "      <td>21.672693</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17286</th>\n",
              "      <td>2021-08-30 18:45:07.460000038</td>\n",
              "      <td>21.670637</td>\n",
              "      <td>21.668586</td>\n",
              "      <td>21.672685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17287</th>\n",
              "      <td>2021-08-30 19:45:07.460000038</td>\n",
              "      <td>21.670233</td>\n",
              "      <td>21.667773</td>\n",
              "      <td>21.672714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17288</th>\n",
              "      <td>2021-08-30 20:45:07.460000038</td>\n",
              "      <td>21.669830</td>\n",
              "      <td>21.666746</td>\n",
              "      <td>21.672819</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17289</th>\n",
              "      <td>2021-08-30 21:45:07.460000038</td>\n",
              "      <td>21.669426</td>\n",
              "      <td>21.665832</td>\n",
              "      <td>21.673041</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                 ds       yhat  yhat_lower  yhat_upper\n",
              "17275 2021-08-30 11:44:47.460000038  21.673464   21.672985   21.673980\n",
              "17276 2021-08-30 11:44:52.460000038  21.673463   21.672982   21.673949\n",
              "17277 2021-08-30 11:44:57.460000038  21.673463   21.672991   21.673951\n",
              "17278 2021-08-30 11:45:02.460000038  21.673462   21.672974   21.673938\n",
              "17279 2021-08-30 11:45:07.460000038  21.673462   21.672976   21.673946\n",
              "17280 2021-08-30 12:45:07.460000038  21.673058   21.672575   21.673570\n",
              "17281 2021-08-30 13:45:07.460000038  21.672655   21.672091   21.673237\n",
              "17282 2021-08-30 14:45:07.460000038  21.672251   21.671528   21.672998\n",
              "17283 2021-08-30 15:45:07.460000038  21.671848   21.670893   21.672894\n",
              "17284 2021-08-30 16:45:07.460000038  21.671444   21.670207   21.672723\n",
              "17285 2021-08-30 17:45:07.460000038  21.671040   21.669440   21.672693\n",
              "17286 2021-08-30 18:45:07.460000038  21.670637   21.668586   21.672685\n",
              "17287 2021-08-30 19:45:07.460000038  21.670233   21.667773   21.672714\n",
              "17288 2021-08-30 20:45:07.460000038  21.669830   21.666746   21.672819\n",
              "17289 2021-08-30 21:45:07.460000038  21.669426   21.665832   21.673041"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 865
        },
        "id": "We3EEIZICsrM",
        "outputId": "586aa742-57d3-4ffd-a479-c99897efedf5"
      },
      "source": [
        "model.plot(forecast_data)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3uFMeyLADWrk"
      },
      "source": [
        "forecast_data_orig = forecast_data # make sure we save the original forecast data\n",
        "forecast_data_orig['yhat'] = np.exp(forecast_data_orig['yhat'])\n",
        "forecast_data_orig['yhat_lower'] = np.exp(forecast_data_orig['yhat_lower'])\n",
        "forecast_data_orig['yhat_upper'] = np.exp(forecast_data_orig['yhat_upper'])\n"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 865
        },
        "id": "IzHQuF1tDyD8",
        "outputId": "8664036f-aa56-4b8d-ebd6-067bf8581e24"
      },
      "source": [
        "model.plot(forecast_data_orig)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RkAEAICMgAwBARkAGAICMgAwAABkBGQAAMgIyAABkBGQAAMgIyAAAkBGQAQAgIyADAEBGQAYAgIyADAAAGQEZAAAyAjIAAGQEZAAAyFQtIK9duzYWLFgQM2bMiJaWlrjhhhsGbJNSiiuuuCKam5tj1qxZ8dhjj1WrHAAAGJL6qh24vj6++MUvRmtra2zbti3mzp0b55xzTsyYMaO8zU9+8pNYtWpVrFq1Kh5++OH4wAc+EA8//HC1SgIAgD2q2ivIkydPjtbW1oiIOPLII2P69Omxfv36ftusXLkyLr744igUCnHaaafF888/Hxs2bKhWSQAAsEdVewU5t3r16nj88cfj1FNP7bd8/fr1cdxxx5UfNzU1xfr162Py5Mn9tmtvb4/29vaIiNi4cWN0dHRUv+gh2LRpU61LGJH0pTK9qUxvKtObwelLZXpTmd5Upjd9qh6Qt2/fHueee258+ctfjnHjxu3TMdra2qKtrS0iIubNmxcNDQ0HssT9MpJqGUn0pTK9qUxvKtObwelLZXpTmd5UpjfdqvpXLDo7O+Pcc8+NCy+8MBYvXjxgfWNjY6xdu7b8eN26ddHY2FjNkgAAYLeqFpBTSnHppZfG9OnT40Mf+tCg2yxatChuu+22SCnFQw89FOPHjx8wvQIAAIZT1aZY/PznP49vfetbMXPmzJgzZ05ERHzuc5+LZ555JiIiLrvssli4cGHcfffd0dzcHGPHjo1bbrmlWuUAAMCQVC0gn3766ZFS2u02hUIhvva1r1WrBAAA2Gs+SQ8AADICMgAAZARkAADICMgAAJARkAEAICMgAwBARkAGAICMgAwAABkBGQAAMgIyAABkBGQAAMgIyAAAkBGQAQAgIyADAEBGQAYAgIyADAAAGQEZAAAyAjIAAGQEZAAAyAjIAACQEZABACAjIAMAQEZABgCAjIAMAAAZARkAADICMgAAZARkAADICMgAAJARkAEAICMgAwBARkAGAICMgAwAABkBGQAAMgIyAABkBGQAAMgIyAAAkBGQAQAgIyADAEBGQAYAgIyADAAAGQEZAAAyAjIAAGQEZAAAyAjIAACQEZABACAjIAMAQEZABgCAjIAMAAAZARkAADICMgAAZARkAADICMgAAJARkAEAICMgAwBARkAGAIBM1QLysmXLYtKkSXHyyScPuv6BBx6I8ePHx5w5c2LOnDlxzTXXVKsUAAAYsvpqHfh973tfLF++PC6++OKK25xxxhlx5513VqsEAADYa1V7BfnMM8+MCRMmVOvwAABQFVV7BXkofvnLX8bs2bOjoaEhrr/++mhpaRl0u/b29mhvb4+IiI0bN0ZHR8dwllnRpk2bal3CiKQvlelNZXpTmd4MTl8q05vK9KYyvelTs4Dc2toaa9asiSOOOCLuvvvueNe73hWrVq0adNu2trZoa2uLiIh58+ZFQ0PDcJa6WyOplpFEXyrTm8r0pjK9GZy+VKY3lelNZXrTrWZ/xWLcuHFxxBFHRETEwoULo7OzMzZv3lyrcgAAICJqGJA3btwYKaWIiHjkkUeiVCrFxIkTa1UOAABERBWnWCxdujQeeOCB2Lx5czQ1NcWnP/3p6OzsjIiIyy67LL7//e/HTTfdFPX19XHYYYfFihUrolAoVKscAAAYkqoF5O9+97u7Xb98+fJYvnx5tU4PAAD7xCfpAQBARkAGAICMgAwAABkBGQAAMgIyAABkBGQAAMgIyAAAkBGQAQAgIyADAEBGQAYAgIyADAAAGQEZAAAyAjIAAGQEZAAAyAjIAACQEZABACAjIAMAQGaPAfnGG2+MrVu3DkctAABQc3sMyM8++2zMnz8/3vOe98Q999wTKaXhqAsAAGpijwH5M5/5TKxatSouvfTSuPXWW2PatGnx8Y9/PJ566qnhqA8AAIbVkOYgFwqFOPbYY+PYY4+N+vr62Lp1a5x33nlx1VVXVbs+AAAYVvV72uCGG26I2267LY4++uh4//vfH1/4whfikEMOiVKpFNOmTYvPf/7zw1EnAAAMiz0G5Oeeey7uuOOOeOMb39hveV1dXdx5551VKwwAAGphjwH505/+dMV106dPP6DFAABArfk7yAAAkBGQAQAgIyADAEBGQAYAgIyADAAAGQEZAAAyAjIAAGQEZAAAyAjIAACQEZABACAjIAMAQEZABgCAjIAMAAAZARkAADICMgAAZARkAADICMgAAJARkAEAICMgAwBARkAGAICMgAwAABkBGQAAMgIyAABkBGQAAMgIyAAAkBGQAQAgIyADAEBGQAYAgIyADAAAGQEZAAAyAjIAAGSqFpCXLVsWkyZNipNPPnnQ9SmluOKKK6K5uTlmzZoVjz32WLVKAQCAIataQH7f+94X99xzT8X1P/nJT2LVqlWxatWqaG9vjw984APVKgUAAIasagH5zDPPjAkTJlRcv3Llyrj44oujUCjEaaedFs8//3xs2LChWuUAAMCQ1NfqxOvXr4/jjjuu/LipqSnWr18fkydPHrBte3t7tLe3R0TExo0bo6OjY9jq3J1NmzbVuoQRSV8q05vK9KYyvRmcvlSmN5XpTWV606dmAXlvtLW1RVtbW0REzJs3LxoaGmpcUZ+RVMtIoi+V6U1lelOZ3gxOXyrTm8r0pjK96Vazv2LR2NgYa9euLT9et25dNDY21qocAACIiBoG5EWLFsVtt90WKaV46KGHYvz48YNOrwAAgOFUtSkWS5cujQceeCA2b94cTU1N8elPfzo6OzsjIuKyyy6LhQsXxt133x3Nzc0xduzYuOWWW6pVCgAADFnVAvJ3v/vd3a4vFArxta99rVqnBwCAfeKT9AAAICMgAwBARkAGAICMgAwAABkBGQAAMgIyAABkBGQAAMgIyAAAkBGQAQAgIyADAEBGQAYAgIyADAAAGQEZAAAyAjIAAGQEZAAAyAjIAACQEZABACAjIAMAQEZABgCAjIAMAAAZARkAADICMgAAZARkAADICMgAAJARkAEAICMgAwBARkAGAICMgAwAABkBGQAAMgIyAABkBGQAAMgIyAAAkBGQAQAgIyADAEBGQAYAgIyADAAAGQEZAAAyAjIAAGQEZAAAyAjIAACQEZABACAjIAMAQEZABgCAjIAMAAAZARkAADICMgAAZARkAADICMgAAJARkAEAICMgAwBARkAGAICMgAwAABkBGQAAMgIyAABkBGQAAMhUNSDfc889ceKJJ0Zzc3Ncd911A9bfeuutccwxx8ScOXNizpw5cfPNN1ezHAAA2KP6ah24WCzG5ZdfHj/96U+jqakp5s+fH4sWLYoZM2b02+6CCy6Ir371q9UqAwAA9krVXkF+5JFHorm5OaZOnRqjR4+OJUuWxMqVK6t1OgAAOCCq9gry+vXr47jjjis/bmpqiocffnjAdrfffnv813/9V5xwwgnxpS99qd8+vdrb26O9vT0iIjZu3BgdHR3VKnuvbNq0qdYljEj6UpneVKY3lenN4PSlMr2pTG8q05s+VQvIQ/E3f/M3sXTp0hgzZkx8/etfj0suuSTuv//+Adu1tbVFW1tbRETMmzcvGhoahrvUikZSLSOJvlSmN5XpTWV6Mzh9qUxvKtObyvSmW9WmWDQ2NsbatWvLj9etWxeNjY39tpk4cWKMGTMmIiLe//73x6OPPlqtcgAAYEiqFpDnz58fq1atiqeffjp27twZK1asiEWLFvXbZsOGDeXvf/SjH8X06dOrVQ4AAAxJ1aZY1NfXx1e/+tV4+9vfHsViMZYtWxYtLS1x9dVXx7x582LRokXxla98JX70ox9FfX19TJgwIW699dZqlQMAAENS1TnICxcujIULF/Zbds0115S/v/baa+Paa6+tZgkAALBXfJIeAABkBGQAAMgIyAAAkBGQAQAgIyADAEBGQAYAgIyADAAAGQEZAAAyAjIAAGQEZAAAyAjIAACQEZABACAjIAMAQEZABgCAjIAMAAAZARkAADICMgAAZARkAADICMgAAJARkAEAICMgAwBARkAGAICMgAwAABkBGQAAMgIyAABkBGQAAMgIyAAAkBGQAQAgIyADAEBGQAYAgIyADAAAGQEZAAAyAjIAAGQEZAAAyAjIAACQEZABACAjIAMAQEZABgCAjIAMAAAZARkAADICMgAAZARkAADICMgAAJARkAEAICMgAwBARkAGAICMgAwAABkBGQAAMgIyAABkBGQAAMgIyAAAkBGQAQAgIyADAEBGQAYAgIyADAAAmaoG5HvuuSdOPPHEaG5ujuuuu27A+h07dsQFF1wQzc3Nceqpp8bq1aurWQ4AAOxRfbUOXCwW4/LLL4+f/vSn0dTUFPPnz49FixbFjBkzytt84xvfiNe97nXxhz/8IVasWBEf+chH4t///d+rVdI+mzhxYjz33HO1LgMAKnrjG9/ohSY4QKr2CvIjjzwSzc3NMXXq1Bg9enQsWbIkVq5c2W+blStXxiWXXBIREeedd17cd999kVKqVkn7RDgG4GCwZs2amDJlSq3LgNeEqr2CvH79+jjuuOPKj5uamuLhhx+uuE19fX2MHz8+tmzZEkcffXS/7drb26O9vT0iIjZu3BgdHR3VKnsA4RiAg8UzzzwTHR0dsWnTplqXMmLpTWV606dqAflAamtri7a2toiImDdvXjQ0NAzbuSdMmCAkA3BQeMMb3lD+HTmcvysPNnpTmd50q9oUi8bGxli7dm358bp166KxsbHiNl1dXfHCCy/ExIkTq1XSPtmyZUtMmDCh1mUAwG6ZgwwHTtVeQZ4/f36sWrUqnn766WhsbIwVK1bEv/3bv/XbZtGiRfGv//qv8eY3vzm+//3vx1lnnRWFQqFaJe2zLVu2DLq8o6PDSGsQ+lKZ3lSmN5XpzeD0BaiWqgXk+vr6+OpXvxpvf/vbo1gsxrJly6KlpSWuvvrqmDdvXixatCguvfTSeO973xvNzc0xYcKEWLFiRbXKAQCAIanqHOSFCxfGwoUL+y275ppryt8feuih8b3vfa+aJQAAwF7xSXoAAJARkAEAICMgAwBARkAGAICMgAwAABkBGQAAMgIyAABkBGQAAMgIyAAAkBGQAQAgIyADAECmkFJKtS5ibxx99NExZcqUWpcRERGbNm2KY445ptZljDj6UpneVKY3lenN4PSlMr2pTG8q+3PszerVq2Pz5s0Dlh90AXkkmTdvXvz617+udRkjjr5UpjeV6U1lejM4falMbyrTm8r0po8pFgAAkBGQAQAgM+pTn/rUp2pdxMFs7ty5tS5hRNKXyvSmMr2pTG8Gpy+V6U1lelOZ3nQzBxkAADKmWAAAQEZABgCAzJ9FQL7nnnvixBNPjObm5rjuuuvKy++7775obW2NOXPmxOmnnx5/+MMfBuz78ssvxzvf+c446aSToqWlJT760Y+W161ZsybOPvvsmDVrVrz1rW+NdevWDXr+ZcuWxaRJk+Lkk0/ut/zDH/5wnHTSSTFr1qx497vfHc8///wBuuKhG6m9iYi48cYby8e+6qqrDsDV7p1a9mbt2rWxYMGCmDFjRrS0tMQNN9xQXvfcc8/FOeecE9OmTYtzzjkntm7deoCvfM/2pzcREe94xzti9uzZ0dLSEpdddlkUi8WIGPq1XXvttdHc3Bwnnnhi3HvvvXusa7iM1L5ERBSLxTjllFPir//6rw/Q1e6dkdqbL33pS9HS0hInn3xyLF26NF599dUDeNVDU8vebNmyJRYsWBBHHHFELF++vLx8d89hw2kk9iYiYufOndHW1hYnnHBCnHTSSXH77bcfwKves1r25ac//WnMnTs3Zs6cGXPnzo3777+/vO7RRx+NmTNnRnNzc1xxxRVxUM/iTa9xXV1daerUqempp55KO3bsSLNmzUpPPPFESimladOmpd///vcppZS+9rWvpUsuuWTA/i+99FK6//77U0op7dixI51++unp7rvvTimldN5556Vbb701pZTSfffdly666KJBa/jZz36WHn300dTS0tJv+b333ps6OztTSildddVV6aqrrtr/C94LI7k3999/fzr77LPTq6++mlJK6dlnn93/C94Lte5NR0dHevTRR1NKKb344otp2rRp5fN/+MMfTtdee21KKaVrr732oPu5SdGade0AAAoeSURBVCmlF154IaWUUqlUSosXL07f/e53U0pDu7YnnngizZo1K7366qvpj3/8Y5o6dWrq6urabV3DYaT2pdcXv/jFtHTp0vTOd77zgF3zUI3U3qxbty5NmTIlvfzyyymllM4///x0yy23HMhL36Na92b79u3pwQcfTDfddFO6/PLLy8t39xw2XEZqb1JK6eqrr06f+MQnUkopFYvFtGnTpv2/4CGqdV8ee+yxtH79+pRSSv/zP/+TGhoayuvmz5+ffvnLX6ZSqZTe8Y53DPvPzIH0mn8F+ZFHHonm5uaYOnVqjB49OpYsWRIrV66MiIhCoRAvvvhiRES88MIL0dDQMGD/sWPHxoIFCyIiYvTo0dHa2lp+xe/3v/99nHXWWRERsWDBgvJxd3XmmWfGhAkTBix/29veFvX19RERcdppp1V8lbVaRnJvbrrppvjoRz8aY8aMiYiISZMm7efV7p1a92by5MnR2toaERFHHnlkTJ8+PdavXx8REStXroxLLrkkIiIuueSS+OEPf3ggL32P9rc3ERHjxo2LiIiurq7YuXNnFAqFiBjata1cuTKWLFkSY8aMieOPPz6am5vjkUce2W1dw2Gk9iUiYt26dXHXXXfF+9///gN70UM0knvT1dUVr7zySnR1dcXLL79c8fzVUuveHH744XH66afHoYce2m/57p7DhstI7U1ExDe/+c342Mc+FhERdXV1cfTRR+/n1Q5drftyyimnlI/b0tISr7zySuzYsSM2bNgQL774Ypx22mlRKBTi4osvHvbfTwfSaz4gr1+/Po477rjy46ampnLQuPnmm2PhwoXR1NQU3/rWt/Z4C+n555+PH//4x3H22WdHRMTs2bPjjjvuiIiIH/zgB7Ft27bYsmXLPtX5zW9+M/7qr/5qn/bdVyO5N//3f/8XDz74YJx66qnxlre8JX71q1/t7eXtl5HUm9WrV8fjjz8ep556akREPPvsszF58uSIiDj22GPj2Wef3fcL3QcHqjdvf/vbY9KkSXHkkUfGeeedFxFDu7ZK599dXcNhpPYlIuKDH/xgfP7zn4+6uto85Y/U3jQ2NsaVV14Zb3jDG2Ly5Mkxfvz4eNvb3nZArnmoat2bodj1OWy4jNTe9E6H/OQnPxmtra1x/vnnD+vz8Ejqy+233x6tra0xZsyYWL9+fTQ1NQ1a18HoNR+Qd+dLX/pS3H333bFu3br4u7/7u/jQhz5Ucduurq5YunRpXHHFFTF16tSIiLj++uvjZz/7WZxyyinxs5/9LBobG2PUqFF7XcdnP/vZqK+vjwsvvHCfr+VAq3Vvurq64rnnnouHHnoovvCFL8R73vOeETOXaTh7s3379jj33HPjy1/+cnnEnysUCuWR/0iwN7259957Y8OGDbFjx45+c9h6jbRr2x+17Mudd94ZkyZNGrF/27SWvdm6dWusXLkynn766ejo6IiXXnopvv3tb+/TdVTDSPj/abDnsJGglr3p6uqKdevWxV/+5V/GY489Fm9+85vjyiuv3KfrONCGsy9PPPFEfOQjH4mvf/3rB6T2keY1H5AbGxtj7dq15cfr1q2LxsbG2LRpU/z2t78tvyp3wQUXxC9+8YsoFosxZ86cmDNnTlx99dXl/dra2mLatGnxwQ9+sLysoaEh7rjjjnj88cfjs5/9bEREHHXUUXtV36233hp33nlnfOc73xn2MDCSe9PU1BSLFy+OQqEQf/EXfxF1dXWxefPm/b3kIRsJvens7Ixzzz03Lrzwwli8eHF5+etf//rYsGFDRERs2LBh2KefHKjeREQceuih8bd/+7fl24NDubZK56+0fLiM1L78/Oc/jx/96EcxZcqUWLJkSdx///1x0UUXHfDr352R2pv/+I//iOOPPz6OOeaYOOSQQ2Lx4sXxi1/84oBf/+7Uujd7Mthz2HAZqb2ZOHFijB07tvy8fP7558djjz22X9e6N0ZCX9atWxfvfve747bbbos3velN5bryaTjD/Rx8wNV6EnS1dXZ2puOPPz798Y9/LE9m/93vfpc6OzvTxIkT05NPPplSSunmm29OixcvHvQYn/jEJ9LixYtTsVjst3zTpk3lZR//+MfTJz/5yYp1PP300wPeiPaTn/wkTZ8+Pf3pT3/an0vcZyO5NzfddFN5nyeffDI1NTWlUqm0z9e6t2rdm1KplN773vemf/zHfxyw7sorr+z3JooPf/jD+3Wte2t/e7Nt27bU0dFRPtZ73vOedOONN6aUhnZtv/vd7/q94er4449PXV1dFesaLiO1L7n//M//rMmb9EZqbx566KE0Y8aM9NJLL6VSqZQuvvji9JWvfKVabRhUrXvT65ZbbhnwRrRKz2HDZST35oILLkj33Xdfef155523/xc8RLXuy9atW9OsWbPS7bffPmDdrm/Su+uuuw7YdQ+313xATimlu+66K02bNi1NnTo1feYznykvv+OOO9LJJ5+cZs2ald7ylrekp556asC+a9euTRGRTjrppDR79uw0e/bs9C//8i8ppZS+973vpebm5jRt2rR06aWXlv/iwq6WLFmSjj322FRfX58aGxvTzTffnFJK6U1velNqamoqH/fv//7vq3D1uzdSe7Njx4504YUXppaWlnTKKaeUn4iGUy178+CDD6aISDNnzizv3/tEs3nz5nTWWWel5ubmdPbZZ6ctW7ZUqQOV7U9vNm7cmObNm5dmzpyZWlpa0vLly8t/zWWo1/aZz3wmTZ06NZ1wwgn93iVdqa7hMlL70qtWATmlkdubq6++Op144omppaUlXXTRRRWfq6qp1r154xvfmF73utelww8/PDU2NqYnnnhit89hw2kk9iallFavXp3OOOOMNHPmzHTWWWelNWvWVOHqK6tlX/7pn/4pjR07tvxzMXv27PJfmvrVr36VWlpa0tSpU9Pll18+rC9sHWg+ahoAADKv+TnIAACwNwRkAADICMgAAJARkAEAICMgAwBARkAGeI351Kc+Fddff32tywA4aAnIAACQEZABXgM++9nPxgknnBCnn356PPnkkxER8ZWvfCVmzJgRs2bNiiVLltS4QoCDR32tCwBg/zz66KOxYsWK+M1vfhNdXV3R2toac+fOjeuuuy6efvrpGDNmTDz//PO1LhPgoOEVZICD3IMPPhjvfve7Y+zYsTFu3LhYtGhRRETMmjUrLrzwwvj2t78d9fVeDwEYKgEZ4DXqrrvuissvvzwee+yxmD9/fnR1ddW6JICDgoAMcJA788wz44c//GG88sorsW3btvjxj38cpVIp1q5dGwsWLIh//ud/jhdeeCG2b99e61IBDgruuQEc5FpbW+OCCy6I2bNnx6RJk2L+/PlRKBTioosuihdeeCFSSnHFFVfEUUcdVetSAQ4KhZRSqnURAAAwUphiAQAAGQEZAAAyAjIAAGQEZAAAyAjIAACQEZABACAjIAMAQOb/B8DlXRpfXIIBAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YRsOFxGaEIvF"
      },
      "source": [
        "df1['y_log']=df1['y'] #copy the log-transformed data to another column\n",
        "df1['y']=df1['y_orig']"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vNilfQcuEOrX"
      },
      "source": [
        "final_df = pd.DataFrame(forecast_data_orig)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wK2JJFjrEVgM"
      },
      "source": [
        "import plotly.graph_objs as go\n",
        "import plotly.offline as py"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "duTuPrfjEcKc",
        "outputId": "a236953e-8ff5-4c01-ad0a-11a74550b917"
      },
      "source": [
        "#Plot predicted and actual line graph with X=dates, Y=Outbound\n",
        "actual_chart = go.Scatter(y=df1[\"y_orig\"], name= 'Actual')\n",
        "predict_chart = go.Scatter(y=final_df[\"yhat\"], name= 'Predicted')\n",
        "predict_chart_upper = go.Scatter(y=final_df[\"yhat_upper\"], name= 'Predicted Upper')\n",
        "predict_chart_lower = go.Scatter(y=final_df[\"yhat_lower\"], name= 'Predicted Lower')\n",
        "py.plot([actual_chart, predict_chart, predict_chart_upper, predict_chart_lower], image_width=400, image_height=400)\n",
        "#py.plot([actual_chart, predict_chart, predict_chart_upper, predict_chart_lower], filename = 'templates/' +'filename.html', auto_open=False)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'temp-plot.html'"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "Ttct6IixGC58",
        "outputId": "1612aa7d-404e-47d5-e799-b53d4034a709"
      },
      "source": [
        "pwd"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/content'"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    }
  ]
}