Old-Wells-Prediction.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "### 1. Importing packages"
      ],
      "metadata": {
        "id": "6K222igTq7OA"
      },
      "id": "6K222igTq7OA"
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install statsmodels==0.13.1"
      ],
      "metadata": {
        "id": "UIcQuN1BWxAC"
      },
      "id": "UIcQuN1BWxAC",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import warnings\n",
        "warnings.filterwarnings(action='ignore')"
      ],
      "metadata": {
        "id": "FICLAL-EQ2vN"
      },
      "id": "FICLAL-EQ2vN",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.tsa.api import SimpleExpSmoothing, Holt, ExponentialSmoothing"
      ],
      "metadata": {
        "id": "8gtwYUgJWzlZ"
      },
      "id": "8gtwYUgJWzlZ",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2. Loading dataset"
      ],
      "metadata": {
        "id": "he3ssTNlq_so"
      },
      "id": "he3ssTNlq_so"
    },
    {
      "cell_type": "code",
      "source": [
        "exam_df = pd.read_csv('examSet.csv', index_col=1)\n",
        "old_wells_df = exam_df.loc[291:, 'GAS_MONTH_1':'GAS_MONTH_30']\n",
        "old_wells_df = old_wells_df.T\n",
        "\n",
        "index = pd.date_range(start='2019-01-01', freq='M', periods=30)\n",
        "old_wells_df.index = index\n",
        "\n",
        "# replacing 0 with 0.1 for multiplicative models\n",
        "old_wells_df.replace(0, 0.1, inplace=True)\n",
        "\n",
        "old_wells_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vIXpvgpuQpd_",
        "outputId": "e8d49c41-0164-4139-9870-5a5ac703ad34"
      },
      "id": "vIXpvgpuQpd_",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(30, 29)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3. Modeling"
      ],
      "metadata": {
        "id": "C7KaxoEMsy0M"
      },
      "id": "C7KaxoEMsy0M"
    },
    {
      "cell_type": "code",
      "source": [
        "# months to be forecasted\n",
        "n = 6\n",
        "\n",
        "# matplot figure size\n",
        "fig_size = (12, 8)"
      ],
      "metadata": {
        "id": "YqaJBnHESXNd"
      },
      "id": "YqaJBnHESXNd",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Simple exponential smoothing"
      ],
      "metadata": {
        "id": "9kqIh86IYgzZ"
      },
      "id": "9kqIh86IYgzZ"
    },
    {
      "cell_type": "code",
      "source": [
        "def ses(well_data):\n",
        "    # fitting and forecasting with different alphas (smoothing levels)\n",
        "    alphas = [0.2, 0.6]\n",
        "\n",
        "    # alpha=0.2\n",
        "    ses_fit1 = SimpleExpSmoothing(well_data, initialization_method='heuristic').fit(\n",
        "                                  smoothing_level=alphas[0], optimized=False)\n",
        "    ses_fcast1 = ses_fit1.forecast(n).rename(r'$\\alpha=0.2$')\n",
        "\n",
        "    # alpha=0.6\n",
        "    ses_fit2 = SimpleExpSmoothing(well_data, initialization_method='heuristic').fit(\n",
        "                                  smoothing_level=alphas[1], optimized=False)\n",
        "    ses_fcast2 = ses_fit2.forecast(n).rename(r'$\\alpha=0.6$')\n",
        "\n",
        "    # letting statsmodels automatically find optimized alpha parameter\n",
        "    ses_fit3 = SimpleExpSmoothing(well_data, initialization_method='estimated').fit()\n",
        "    optim_alpha = ses_fit3.model.params['smoothing_level']\n",
        "    ses_fcast3 = ses_fit3.forecast(n).rename(r'$\\alpha={}$'.format(optim_alpha))\n",
        "\n",
        "    # plotting fitted and forecasted values\n",
        "    plt.figure(figsize=fig_size)\n",
        "    plt.plot(well_data, marker='o', color='black')\n",
        "\n",
        "    plt.plot(ses_fit1.fittedvalues, marker='o', color='blue')\n",
        "    (line1,) = plt.plot(ses_fcast1, marker='o', color='blue')\n",
        "\n",
        "    plt.plot(ses_fit2.fittedvalues, marker='o', color='red')\n",
        "    (line2,) = plt.plot(ses_fcast2, marker='o', color='red')\n",
        "\n",
        "    plt.plot(ses_fit3.fittedvalues, marker='o', color='green')\n",
        "    (line3,) = plt.plot(ses_fcast3, marker='o', color='green')\n",
        "\n",
        "    plt.title('SES')\n",
        "    plt.legend([line1, line2, line3], \n",
        "               [ses_fcast1.name, ses_fcast2.name, ses_fcast3.name])\n",
        "\n",
        "    return ses_fit1, ses_fit2, ses_fit3"
      ],
      "metadata": {
        "id": "9ISTkDO7VSfn"
      },
      "id": "9ISTkDO7VSfn",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Holt’s method"
      ],
      "metadata": {
        "id": "s7gJqmFMZ424"
      },
      "id": "s7gJqmFMZ424"
    },
    {
      "cell_type": "code",
      "source": [
        "def holt(well_data):\n",
        "    # letting statsmodels automatically find optimized parameters\n",
        "\n",
        "    # Holt's additive model - double exponential smoothing with linear model\n",
        "    holt_fit1 = Holt(well_data, initialization_method='estimated').fit()\n",
        "    holt_fcast1 = holt_fit1.forecast(n).rename('Additive')\n",
        "\n",
        "    # Holt's multiplicative model - double exponential smoothing with exponential model\n",
        "    holt_fit2 = Holt(well_data, exponential=True, initialization_method='estimated').fit()\n",
        "    holt_fcast2 = holt_fit2.forecast(n).rename('Multiplicative')\n",
        "\n",
        "    # Damped Holt's additive model\n",
        "    holt_fit3 = Holt(well_data, damped_trend=True, initialization_method='estimated').fit()\n",
        "    holt_fcast3 = holt_fit3.forecast(n).rename('Additive Damped')\n",
        "\n",
        "    # Damped Holt's multiplicative model\n",
        "    holt_fit4 = Holt(well_data, exponential=True, damped_trend=True, initialization_method='estimated').fit()\n",
        "    holt_fcast4 = holt_fit4.forecast(n).rename('Multiplicative Damped')\n",
        "\n",
        "    # plotting fitted and forecasted values\n",
        "    plt.figure(figsize=fig_size)\n",
        "    plt.plot(well_data, marker='o', color='black')\n",
        "\n",
        "    plt.plot(holt_fit1.fittedvalues, marker='o', color='blue')\n",
        "    (line1,) = plt.plot(holt_fcast1, marker='o', color='blue')\n",
        "\n",
        "    plt.plot(holt_fit2.fittedvalues, marker='o', color='red')\n",
        "    (line2,) = plt.plot(holt_fcast2, marker='o', color='red')\n",
        "\n",
        "    plt.plot(holt_fit3.fittedvalues, marker='o', color='green')\n",
        "    (line3,) = plt.plot(holt_fcast3, marker='o', color='green')\n",
        "\n",
        "    plt.plot(holt_fit4.fittedvalues, marker='o', color='cyan')\n",
        "    (line4,) = plt.plot(holt_fcast4, marker='o', color='cyan')\n",
        "\n",
        "    plt.title('Holt\\'s Method')\n",
        "    plt.legend([line1, line2, line3, line4], \n",
        "               [holt_fcast1.name, holt_fcast2.name, holt_fcast3.name, holt_fcast4.name])\n",
        "    \n",
        "    return holt_fit1, holt_fit2, holt_fit3, holt_fit4"
      ],
      "metadata": {
        "id": "NuDNB206Y75j"
      },
      "id": "NuDNB206Y75j",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Exponential smoothing"
      ],
      "metadata": {
        "id": "Qx6ZssxdlFEs"
      },
      "id": "Qx6ZssxdlFEs"
    },
    {
      "cell_type": "code",
      "source": [
        "def es(well_data):\n",
        "    # fitting and forecasting additive model with and without damping\n",
        "    es_fit1 = ExponentialSmoothing(well_data, \n",
        "                                   trend='add', \n",
        "                                   use_boxcox=True, \n",
        "                                   initialization_method='estimated').fit()\n",
        "    es_fcast1 = es_fit1.forecast(n).rename('Additive')\n",
        "\n",
        "    es_fit2 = ExponentialSmoothing(well_data, \n",
        "                                   trend='add', \n",
        "                                   damped_trend=True, \n",
        "                                   use_boxcox=True, \n",
        "                                   initialization_method='estimated').fit()\n",
        "    es_fcast2 = es_fit2.forecast(n).rename('Additive Damped')\n",
        "\n",
        "    # plotting fitted and forecasted values\n",
        "    plt.figure(figsize=fig_size)\n",
        "    plt.plot(well_data, marker='o', color='black')\n",
        "\n",
        "    plt.plot(es_fit1.fittedvalues, marker='o', color='blue')\n",
        "    (line1,) = plt.plot(es_fcast1, marker='o', color='blue')\n",
        "\n",
        "    plt.plot(es_fit2.fittedvalues, marker='o', color='red')\n",
        "    (line2,) = plt.plot(es_fcast2, marker='o', color='red')\n",
        "\n",
        "    plt.title('Exponential Smoothing')\n",
        "    plt.legend([line1, line2], \n",
        "               [es_fcast1.name, es_fcast2.name])\n",
        "\n",
        "    return es_fit1, es_fit2"
      ],
      "metadata": {
        "id": "HKBu6_y5c17R"
      },
      "id": "HKBu6_y5c17R",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4. Fitting models and forecasting"
      ],
      "metadata": {
        "id": "0fUDCoewuhsH"
      },
      "id": "0fUDCoewuhsH"
    },
    {
      "cell_type": "code",
      "source": [
        "# using random data for testing\n",
        "# actual data can't be used due to confidentiality\n",
        "data = [408.2705, 401.4993, 394.6971, 411.9468, 412.2339, 417.7495, 417.1236, \n",
        "        417.5629, 348.3705, 352.2979, 346.3852, 329.7893, 321.4138, 314.4597, \n",
        "        314.2554, 328.6006, 339.2441, 338.884, 329.3724, 318.3311, 309.2867, \n",
        "        300.8307, 292.4742, 290.309, 283.834, 277.5158, 266.6394, 260.6626, \n",
        "        268.3072,263.9177]\n",
        "well_data = pd.Series(data)\n",
        "\n",
        "ax = well_data.plot()\n",
        "ax.set_xlabel('Month')\n",
        "ax.set_ylabel('Gas Production')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "38gszZEuu6Gb",
        "outputId": "254aa5aa-492a-445c-8135-9b84fad4871e"
      },
      "id": "38gszZEuu6Gb",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Gas Production')"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ses_fit1, ses_fit2, ses_fit3 = ses(well_data)\n",
        "holt_fit1, holt_fit2, holt_fit3, holt_fit4 = holt(well_data)\n",
        "es_fit1, es_fit2 = es(well_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "s18uhYNlyjkx",
        "outputId": "d0bf0f31-a78d-49f0-fa98-541c3e80c760"
      },
      "id": "s18uhYNlyjkx",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5. Results"
      ],
      "metadata": {
        "id": "hvAGukWtqBBn"
      },
      "id": "hvAGukWtqBBn"
    },
    {
      "cell_type": "code",
      "source": [
        "# viewing results\n",
        "model_names = ['SES 0.2',\n",
        "               'SES 0.6',\n",
        "               'SES optim',\n",
        "               'Holt\\'s Additive',\n",
        "               'Holt\\'s Multiplicative',\n",
        "               'Holt\\'s Additive Damped',\n",
        "               'Holt\\'s Multiplicative Damped',\n",
        "               'ES Additive',\n",
        "               'ES Additive Damped']\n",
        "\n",
        "params = [\n",
        "    'smoothing_level',\n",
        "    'smoothing_trend',\n",
        "    'damping_trend',\n",
        "    'initial_level',\n",
        "    'initial_trend'\n",
        "]\n",
        "\n",
        "results = pd.DataFrame(index=['alpha', 'beta', 'phi', 'l_0', 'b_0', 'SSE'])\n",
        "\n",
        "results[model_names[0]] = [ses_fit1.params[p] for p in params] + [ses_fit1.sse]\n",
        "results[model_names[1]] = [ses_fit2.params[p] for p in params] + [ses_fit2.sse]\n",
        "results[model_names[2]] = [ses_fit3.params[p] for p in params] + [ses_fit3.sse]\n",
        "\n",
        "results[model_names[3]] = [holt_fit1.params[p] for p in params] + [holt_fit1.sse]\n",
        "results[model_names[4]] = [holt_fit2.params[p] for p in params] + [holt_fit2.sse]\n",
        "results[model_names[5]] = [holt_fit3.params[p] for p in params] + [holt_fit3.sse]\n",
        "results[model_names[6]] = [holt_fit4.params[p] for p in params] + [holt_fit4.sse]\n",
        "\n",
        "results[model_names[7]] = [es_fit1.params[p] for p in params] + [es_fit1.sse]\n",
        "results[model_names[8]] = [es_fit2.params[p] for p in params] + [es_fit2.sse]\n",
        "\n",
        "results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "2otSfSJmfxwP",
        "outputId": "0ed3b220-d0d3-4670-d5a0-4db6f6e73d6f"
      },
      "id": "2otSfSJmfxwP",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SES 0.2</th>\n",
              "      <th>SES 0.6</th>\n",
              "      <th>SES optim</th>\n",
              "      <th>Holt's Additive</th>\n",
              "      <th>Holt's Multiplicative</th>\n",
              "      <th>Holt's Additive Damped</th>\n",
              "      <th>Holt's Multiplicative Damped</th>\n",
              "      <th>ES Additive</th>\n",
              "      <th>ES Additive Damped</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>alpha</th>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.973991</td>\n",
              "      <td>9.770379e-01</td>\n",
              "      <td>9.743544e-01</td>\n",
              "      <td>0.979193</td>\n",
              "      <td>0.995000</td>\n",
              "      <td>0.995002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>beta</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.949755e-10</td>\n",
              "      <td>2.882368e-09</td>\n",
              "      <td>0.000026</td>\n",
              "      <td>0.000099</td>\n",
              "      <td>0.000073</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>phi</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.950000e-01</td>\n",
              "      <td>0.994984</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.990016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>l_0</th>\n",
              "      <td>422.851200</td>\n",
              "      <td>422.851200</td>\n",
              "      <td>408.270507</td>\n",
              "      <td>413.200151</td>\n",
              "      <td>4.145781e+02</td>\n",
              "      <td>4.135685e+02</td>\n",
              "      <td>414.952348</td>\n",
              "      <td>1.699121</td>\n",
              "      <td>1.698438</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b_0</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-4.976880</td>\n",
              "      <td>9.847541e-01</td>\n",
              "      <td>-5.379413e+00</td>\n",
              "      <td>0.988851</td>\n",
              "      <td>-0.000585</td>\n",
              "      <td>-0.000637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SSE</th>\n",
              "      <td>27536.060817</td>\n",
              "      <td>9229.194588</td>\n",
              "      <td>6725.681867</td>\n",
              "      <td>6003.087382</td>\n",
              "      <td>5.929547e+03</td>\n",
              "      <td>6.004477e+03</td>\n",
              "      <td>5941.212543</td>\n",
              "      <td>5990.355742</td>\n",
              "      <td>5938.519767</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0d274656-af8a-4691-8a8a-4fdc9afb9958');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "            SES 0.2      SES 0.6  ...  ES Additive  ES Additive Damped\n",
              "alpha      0.200000     0.600000  ...     0.995000            0.995002\n",
              "beta            NaN          NaN  ...     0.000099            0.000073\n",
              "phi             NaN          NaN  ...          NaN            0.990016\n",
              "l_0      422.851200   422.851200  ...     1.699121            1.698438\n",
              "b_0             NaN          NaN  ...    -0.000585           -0.000637\n",
              "SSE    27536.060817  9229.194588  ...  5990.355742         5938.519767\n",
              "\n",
              "[6 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# getting best model - with minimum SSE\n",
        "models = [ses_fit1, ses_fit2, ses_fit3, \n",
        "          holt_fit1, holt_fit2, holt_fit3, holt_fit4, \n",
        "          es_fit1, es_fit2]\n",
        "sses = results.loc['SSE']\n",
        "\n",
        "sses_dict = {}\n",
        "for i in range(len(models)):\n",
        "    sses_dict[models[i]] = sses[i]\n",
        "\n",
        "min_sse = sses.min()\n",
        "best_model_index = list(sses_dict.values()).index(min_sse)\n",
        "best_model = list(sses_dict.keys())[best_model_index]\n",
        "\n",
        "print(model_names[best_model_index])\n",
        "print(min_sse)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vJUgjQCf0r4Y",
        "outputId": "06d4b2a3-42d5-4736-8806-db6592832c15"
      },
      "id": "vJUgjQCf0r4Y",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Holt's Multiplicative\n",
            "5929.547410282388\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 6. Forecasting"
      ],
      "metadata": {
        "id": "nqyw9m4vr93i"
      },
      "id": "nqyw9m4vr93i"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Test data"
      ],
      "metadata": {
        "id": "KVfpRRd0Paeh"
      },
      "id": "KVfpRRd0Paeh"
    },
    {
      "cell_type": "code",
      "source": [
        "# forecasting last 6 months with best model\n",
        "forecast_df = pd.DataFrame(np.c_[well_data, best_model.level, best_model.fittedvalues], \n",
        "                           columns=['y', 'l', 'y_hat'], \n",
        "                           index=well_data.index)\n",
        "forecast_df = forecast_df.append(best_model.forecast(n).rename('y_hat').to_frame(), sort=True)\n",
        "forecast_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "lMSIDR4DkeLI",
        "outputId": "822670c5-7ef8-4a93-9a8b-e4e10fd05a1e"
      },
      "id": "lMSIDR4DkeLI",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-21905e1a-9cbc-4d6b-9e64-816d93edbb2f\">\n",
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              "\n",
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              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>l</th>\n",
              "      <th>y</th>\n",
              "      <th>y_hat</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>408.270201</td>\n",
              "      <td>408.2705</td>\n",
              "      <td>408.257458</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>401.511848</td>\n",
              "      <td>401.4993</td>\n",
              "      <td>402.045762</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>394.713021</td>\n",
              "      <td>394.6971</td>\n",
              "      <td>395.390446</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>411.412897</td>\n",
              "      <td>411.9468</td>\n",
              "      <td>388.695273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>412.071022</td>\n",
              "      <td>412.2339</td>\n",
              "      <td>405.140545</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>417.474854</td>\n",
              "      <td>417.7495</td>\n",
              "      <td>405.788636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>416.985517</td>\n",
              "      <td>417.1236</td>\n",
              "      <td>411.110082</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>417.403665</td>\n",
              "      <td>417.5629</td>\n",
              "      <td>410.628206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>349.809521</td>\n",
              "      <td>348.3705</td>\n",
              "      <td>411.039979</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>352.118301</td>\n",
              "      <td>352.2979</td>\n",
              "      <td>344.476367</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>346.393575</td>\n",
              "      <td>346.3852</td>\n",
              "      <td>346.749948</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>330.049304</td>\n",
              "      <td>329.7893</td>\n",
              "      <td>341.112500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>321.496546</td>\n",
              "      <td>321.4138</td>\n",
              "      <td>325.017412</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>314.508732</td>\n",
              "      <td>314.4597</td>\n",
              "      <td>316.595048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>314.151115</td>\n",
              "      <td>314.2554</td>\n",
              "      <td>309.713769</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>328.158833</td>\n",
              "      <td>328.6006</td>\n",
              "      <td>309.361604</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>338.874678</td>\n",
              "      <td>339.2441</td>\n",
              "      <td>323.155762</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>338.765154</td>\n",
              "      <td>338.8840</td>\n",
              "      <td>333.708236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>329.469483</td>\n",
              "      <td>329.3724</td>\n",
              "      <td>333.600381</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>318.471521</td>\n",
              "      <td>318.3311</td>\n",
              "      <td>324.446431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>309.386113</td>\n",
              "      <td>309.2867</td>\n",
              "      <td>313.616142</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>300.918841</td>\n",
              "      <td>300.8307</td>\n",
              "      <td>304.669249</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>292.562762</td>\n",
              "      <td>292.4742</td>\n",
              "      <td>296.331068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>290.258332</td>\n",
              "      <td>290.3090</td>\n",
              "      <td>288.102385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>283.879903</td>\n",
              "      <td>283.8340</td>\n",
              "      <td>285.833088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>277.562553</td>\n",
              "      <td>277.5158</td>\n",
              "      <td>279.551904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>266.793050</td>\n",
              "      <td>266.6394</td>\n",
              "      <td>273.330868</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>260.709970</td>\n",
              "      <td>260.6626</td>\n",
              "      <td>262.725555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>268.041483</td>\n",
              "      <td>268.3072</td>\n",
              "      <td>256.735217</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>263.918555</td>\n",
              "      <td>263.9177</td>\n",
              "      <td>263.954955</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>259.894885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>255.932558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>252.030641</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>248.188212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>244.404364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>240.678204</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-21905e1a-9cbc-4d6b-9e64-816d93edbb2f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "      \n",
              "  <style>\n",
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              "      background-color: #E8F0FE;\n",
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              "        buttonEl.style.display =\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ],
            "text/plain": [
              "             l         y       y_hat\n",
              "0   408.270201  408.2705  408.257458\n",
              "1   401.511848  401.4993  402.045762\n",
              "2   394.713021  394.6971  395.390446\n",
              "3   411.412897  411.9468  388.695273\n",
              "4   412.071022  412.2339  405.140545\n",
              "5   417.474854  417.7495  405.788636\n",
              "6   416.985517  417.1236  411.110082\n",
              "7   417.403665  417.5629  410.628206\n",
              "8   349.809521  348.3705  411.039979\n",
              "9   352.118301  352.2979  344.476367\n",
              "10  346.393575  346.3852  346.749948\n",
              "11  330.049304  329.7893  341.112500\n",
              "12  321.496546  321.4138  325.017412\n",
              "13  314.508732  314.4597  316.595048\n",
              "14  314.151115  314.2554  309.713769\n",
              "15  328.158833  328.6006  309.361604\n",
              "16  338.874678  339.2441  323.155762\n",
              "17  338.765154  338.8840  333.708236\n",
              "18  329.469483  329.3724  333.600381\n",
              "19  318.471521  318.3311  324.446431\n",
              "20  309.386113  309.2867  313.616142\n",
              "21  300.918841  300.8307  304.669249\n",
              "22  292.562762  292.4742  296.331068\n",
              "23  290.258332  290.3090  288.102385\n",
              "24  283.879903  283.8340  285.833088\n",
              "25  277.562553  277.5158  279.551904\n",
              "26  266.793050  266.6394  273.330868\n",
              "27  260.709970  260.6626  262.725555\n",
              "28  268.041483  268.3072  256.735217\n",
              "29  263.918555  263.9177  263.954955\n",
              "30         NaN       NaN  259.894885\n",
              "31         NaN       NaN  255.932558\n",
              "32         NaN       NaN  252.030641\n",
              "33         NaN       NaN  248.188212\n",
              "34         NaN       NaN  244.404364\n",
              "35         NaN       NaN  240.678204"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# mean of forecasted last 6 months \n",
        "forecast_df['y_hat'].tail(n).mean()"
      ],
      "metadata": {
        "id": "Ne0qThICn_Kz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ad61f795-de7f-42ad-ec19-63ba453c9635"
      },
      "id": "Ne0qThICn_Kz",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "250.18814411884622"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Actual data"
      ],
      "metadata": {
        "id": "PiTZ2NB7PdtZ"
      },
      "id": "PiTZ2NB7PdtZ"
    },
    {
      "cell_type": "code",
      "source": [
        "# forecasting last 6 months with best models\n",
        "best_models = []\n",
        "avgs = []\n",
        "\n",
        "for w in old_wells_df.columns:\n",
        "    well_data = old_wells_df[w]\n",
        "\n",
        "    ses_fit1, ses_fit2, ses_fit3 = ses(well_data)\n",
        "    holt_fit1, holt_fit2, holt_fit3, holt_fit4 = holt(well_data)\n",
        "    es_fit1, es_fit2 = es(well_data)\n",
        "\n",
        "    models = [ses_fit1, ses_fit2, ses_fit3, \n",
        "              holt_fit1, holt_fit2, holt_fit3, holt_fit4, \n",
        "              es_fit1, es_fit2]\n",
        "    sses = [ses_fit1.sse, ses_fit2.sse, ses_fit3.sse, \n",
        "            holt_fit1.sse, holt_fit2.sse, holt_fit3.sse, holt_fit4.sse, \n",
        "            es_fit1.sse, es_fit2.sse]\n",
        "    \n",
        "    sses_dict = {}\n",
        "    for i in range(len(models)):\n",
        "        sses_dict[models[i]] = sses[i]\n",
        "\n",
        "    best_model_index = list(sses_dict.values()).index(min(sses))\n",
        "    # Holt's Additive giving negative values - replacing with SES 0.6\n",
        "    if best_model_index == 3:\n",
        "        best_model_index = 1\n",
        "    best_model = list(sses_dict.keys())[best_model_index]\n",
        "\n",
        "    best_models.append(model_names[best_model_index])\n",
        "    avgs.append(best_model.forecast(n).mean())"
      ],
      "metadata": {
        "id": "hBziZJyhAKX8"
      },
      "id": "hBziZJyhAKX8",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "forecast_df = pd.read_csv('forecast.csv')\n",
        "forecast_df['Last 6 Avg'] = avgs\n",
        "forecast_df.to_csv('forecast1.csv')"
      ],
      "metadata": {
        "id": "OyhMbL4lQ5FH"
      },
      "id": "OyhMbL4lQ5FH",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "aHlvLAsPWkgS"
      },
      "id": "aHlvLAsPWkgS",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
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      "file_extension": ".py",
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      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.8"
    },
    "colab": {
      "name": "Old-Wells-Prediction.ipynb",
      "provenance": [],
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      "toc_visible": true
    }
  },
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  "nbformat_minor": 5
}