TIME_SERIES_ANALYSIS.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "TIME SERIES ANALYSIS.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  },
  "cells": [
    {
      "metadata": {
        "id": "sqhM6cnaTtWJ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#DATA Source: https://datamarket.com/data/set/22ox/monthly-milk-production-pounds-per-cow-jan-62-dec-75#!ds=22ox&display=line\n",
        "\n",
        "#Monthly milk production: pounds per cow. Jan 62 - Dec 75"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "XL_2EOAeTlZg",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#importing essential libraries.\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "u7-dKy9qTlZn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#reading dataset as pandas datframe.\n",
        "milk=pd.read_csv(\"monthly-milk-production.csv\",index_col=\"Month\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "bdu5S_nbTlZs",
        "colab_type": "code",
        "colab": {},
        "outputId": "e55d7108-3152-4640-cb1d-cf92af783511"
      },
      "cell_type": "code",
      "source": [
        "#dataframe\n",
        "milk"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\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>Milk Production</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1962-01-01 01:00:00</th>\n",
              "      <td>589.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1962-02-01 01:00:00</th>\n",
              "      <td>561.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1962-03-01 01:00:00</th>\n",
              "      <td>640.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1962-04-01 01:00:00</th>\n",
              "      <td>656.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1962-05-01 01:00:00</th>\n",
              "      <td>727.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1962-06-01 01:00:00</th>\n",
              "      <td>697.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1962-07-01 01:00:00</th>\n",
              "      <td>640.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1962-08-01 01:00:00</th>\n",
              "      <td>599.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1962-09-01 01:00:00</th>\n",
              "      <td>568.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1962-10-01 01:00:00</th>\n",
              "      <td>577.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1962-11-01 01:00:00</th>\n",
              "      <td>553.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1962-12-01 01:00:00</th>\n",
              "      <td>582.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1963-01-01 01:00:00</th>\n",
              "      <td>600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1963-02-01 01:00:00</th>\n",
              "      <td>566.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1963-03-01 01:00:00</th>\n",
              "      <td>653.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1963-04-01 01:00:00</th>\n",
              "      <td>673.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1963-05-01 01:00:00</th>\n",
              "      <td>742.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1963-06-01 01:00:00</th>\n",
              "      <td>716.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1963-07-01 01:00:00</th>\n",
              "      <td>660.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1963-08-01 01:00:00</th>\n",
              "      <td>617.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1963-09-01 01:00:00</th>\n",
              "      <td>583.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1963-10-01 01:00:00</th>\n",
              "      <td>587.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1963-11-01 01:00:00</th>\n",
              "      <td>565.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1963-12-01 01:00:00</th>\n",
              "      <td>598.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1964-01-01 01:00:00</th>\n",
              "      <td>628.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1964-02-01 01:00:00</th>\n",
              "      <td>618.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1964-03-01 01:00:00</th>\n",
              "      <td>688.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1964-04-01 01:00:00</th>\n",
              "      <td>705.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1964-05-01 01:00:00</th>\n",
              "      <td>770.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1964-06-01 01:00:00</th>\n",
              "      <td>736.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1973-07-01 01:00:00</th>\n",
              "      <td>881.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1973-08-01 01:00:00</th>\n",
              "      <td>837.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1973-09-01 01:00:00</th>\n",
              "      <td>784.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1973-10-01 01:00:00</th>\n",
              "      <td>791.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1973-11-01 01:00:00</th>\n",
              "      <td>760.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1973-12-01 01:00:00</th>\n",
              "      <td>802.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1974-01-01 01:00:00</th>\n",
              "      <td>828.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1974-02-01 01:00:00</th>\n",
              "      <td>778.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1974-03-01 01:00:00</th>\n",
              "      <td>889.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1974-04-01 01:00:00</th>\n",
              "      <td>902.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1974-05-01 01:00:00</th>\n",
              "      <td>969.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1974-06-01 01:00:00</th>\n",
              "      <td>947.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1974-07-01 01:00:00</th>\n",
              "      <td>908.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1974-08-01 01:00:00</th>\n",
              "      <td>867.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1974-09-01 01:00:00</th>\n",
              "      <td>815.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1974-10-01 01:00:00</th>\n",
              "      <td>812.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1974-11-01 01:00:00</th>\n",
              "      <td>773.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1974-12-01 01:00:00</th>\n",
              "      <td>813.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-01-01 01:00:00</th>\n",
              "      <td>834.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-02-01 01:00:00</th>\n",
              "      <td>782.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-03-01 01:00:00</th>\n",
              "      <td>892.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-04-01 01:00:00</th>\n",
              "      <td>903.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-05-01 01:00:00</th>\n",
              "      <td>966.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-06-01 01:00:00</th>\n",
              "      <td>937.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-07-01 01:00:00</th>\n",
              "      <td>896.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-08-01 01:00:00</th>\n",
              "      <td>858.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-09-01 01:00:00</th>\n",
              "      <td>817.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-10-01 01:00:00</th>\n",
              "      <td>827.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-11-01 01:00:00</th>\n",
              "      <td>797.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-12-01 01:00:00</th>\n",
              "      <td>843.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>168 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                     Milk Production\n",
              "Month                               \n",
              "1962-01-01 01:00:00            589.0\n",
              "1962-02-01 01:00:00            561.0\n",
              "1962-03-01 01:00:00            640.0\n",
              "1962-04-01 01:00:00            656.0\n",
              "1962-05-01 01:00:00            727.0\n",
              "1962-06-01 01:00:00            697.0\n",
              "1962-07-01 01:00:00            640.0\n",
              "1962-08-01 01:00:00            599.0\n",
              "1962-09-01 01:00:00            568.0\n",
              "1962-10-01 01:00:00            577.0\n",
              "1962-11-01 01:00:00            553.0\n",
              "1962-12-01 01:00:00            582.0\n",
              "1963-01-01 01:00:00            600.0\n",
              "1963-02-01 01:00:00            566.0\n",
              "1963-03-01 01:00:00            653.0\n",
              "1963-04-01 01:00:00            673.0\n",
              "1963-05-01 01:00:00            742.0\n",
              "1963-06-01 01:00:00            716.0\n",
              "1963-07-01 01:00:00            660.0\n",
              "1963-08-01 01:00:00            617.0\n",
              "1963-09-01 01:00:00            583.0\n",
              "1963-10-01 01:00:00            587.0\n",
              "1963-11-01 01:00:00            565.0\n",
              "1963-12-01 01:00:00            598.0\n",
              "1964-01-01 01:00:00            628.0\n",
              "1964-02-01 01:00:00            618.0\n",
              "1964-03-01 01:00:00            688.0\n",
              "1964-04-01 01:00:00            705.0\n",
              "1964-05-01 01:00:00            770.0\n",
              "1964-06-01 01:00:00            736.0\n",
              "...                              ...\n",
              "1973-07-01 01:00:00            881.0\n",
              "1973-08-01 01:00:00            837.0\n",
              "1973-09-01 01:00:00            784.0\n",
              "1973-10-01 01:00:00            791.0\n",
              "1973-11-01 01:00:00            760.0\n",
              "1973-12-01 01:00:00            802.0\n",
              "1974-01-01 01:00:00            828.0\n",
              "1974-02-01 01:00:00            778.0\n",
              "1974-03-01 01:00:00            889.0\n",
              "1974-04-01 01:00:00            902.0\n",
              "1974-05-01 01:00:00            969.0\n",
              "1974-06-01 01:00:00            947.0\n",
              "1974-07-01 01:00:00            908.0\n",
              "1974-08-01 01:00:00            867.0\n",
              "1974-09-01 01:00:00            815.0\n",
              "1974-10-01 01:00:00            812.0\n",
              "1974-11-01 01:00:00            773.0\n",
              "1974-12-01 01:00:00            813.0\n",
              "1975-01-01 01:00:00            834.0\n",
              "1975-02-01 01:00:00            782.0\n",
              "1975-03-01 01:00:00            892.0\n",
              "1975-04-01 01:00:00            903.0\n",
              "1975-05-01 01:00:00            966.0\n",
              "1975-06-01 01:00:00            937.0\n",
              "1975-07-01 01:00:00            896.0\n",
              "1975-08-01 01:00:00            858.0\n",
              "1975-09-01 01:00:00            817.0\n",
              "1975-10-01 01:00:00            827.0\n",
              "1975-11-01 01:00:00            797.0\n",
              "1975-12-01 01:00:00            843.0\n",
              "\n",
              "[168 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "metadata": {
        "id": "TCqQ71NnTlZ0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Making index a timeseries.\n",
        "milk.index=pd.to_datetime(milk.index)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "bywLoz6XTlZ5",
        "colab_type": "code",
        "colab": {},
        "outputId": "7d616a9c-181e-4f1c-a3c3-0bf4f21d4e8c"
      },
      "cell_type": "code",
      "source": [
        "milk.plot()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x1dd57e068d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "VbBjYBBgTlZ_",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "___"
      ]
    },
    {
      "metadata": {
        "id": "s4RnqiXEVJrS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#We will predict the 12 months ahead data by using time series analysis."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "O0flJpjzTlaD",
        "colab_type": "code",
        "colab": {},
        "outputId": "24eb2620-6f7a-42eb-fccb-9e8b7bd62f2f"
      },
      "cell_type": "code",
      "source": [
        "milk.info()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 168 entries, 1962-01-01 01:00:00 to 1975-12-01 01:00:00\n",
            "Data columns (total 1 columns):\n",
            "Milk Production    168 non-null float64\n",
            "dtypes: float64(1)\n",
            "memory usage: 2.6 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "YL6ZqgYmTlaJ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Train test split in which last 12 entries will be the test data for comparisons with our predictions. everything else will be training data.\n",
        "train_set=milk.head(156)\n",
        "test_set=milk.tail(12)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "0YQKg4AnTlaS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "T4qQG6KaTlaV",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "scaler=MinMaxScaler()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "r1EhrgJSTlaX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Scaling the data by min_max scaler.Only transforming test by fitting into training data as we dont expect to know future behaviour\n",
        "train_scaled=scaler.fit_transform(train_set)\n",
        "test_scaled=scaler.transform(test_set)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "j94srhw8Tlaa",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-LEFX84gTlae",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#This function creates batches pf training data and returns to batches shifted by 1 time step!\n",
        "def next_batch(training_data,batch_size,steps):\n",
        "    \n",
        "\n",
        "    rand_int=np.random.randint(0,len(training_data)-steps)\n",
        "    batch_data=np.array(training_data[rand_int:rand_int+steps+1]).reshape(1,steps+1)\n",
        "    return batch_data[:,:-1].reshape(-1,steps,1),batch_data[:,1:].reshape(-1,steps,1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "OL1Y68e7Tlam",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_sXOihJtTlaq",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#defining necessary constants.\n",
        "num_inputs=1\n",
        "num_time_steps=12\n",
        "num_neurons=100\n",
        "num_outputs=1\n",
        "learning_rate=0.01\n",
        "num_iter=5000\n",
        "batch_size=1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "f8UGu--BTlaw",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#defining placeholders to feed in batches pf data later in session.\n",
        "X=tf.placeholder(tf.float32,shape=[None,num_time_steps,num_inputs])\n",
        "y=tf.placeholder(tf.float32,shape=[None,num_time_steps,num_outputs])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ncGKAmBRTlaz",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#defining single RNN cell. you can also use BASCICRNN cell or a GRU cell.\n",
        "cell=tf.contrib.rnn.OutputProjectionWrapper(tf.contrib.rnn.BasicLSTMCell(num_units=num_neurons,activation=tf.nn.relu),output_size=num_outputs)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3ZnIwpRtTla2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#rolling out the single cell into different time steps.\n",
        "outputs,states=tf.nn.dynamic_rnn(cell,X,dtype=tf.float32)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "iUIHYYmFTla5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Creating a Mean Squared Error Loss Function and use it to minimize an AdamOptimizer\n",
        "loss=tf.reduce_mean(tf.square(outputs-y))\n",
        "optimizer=tf.train.AdamOptimizer(learning_rate)\n",
        "train=optimizer.minimize(loss)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ue9HWzynTla8",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Initialize the global variables\n",
        "init=tf.global_variables_initializer()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "SkAqk0eRTla_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Creating an instance of tf.train.Saver()\n",
        "saver=tf.train.Saver()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "p5_BzFRCTlbI",
        "colab_type": "code",
        "colab": {},
        "outputId": "e422e8df-56ee-4c21-e902-e0d7c58751e2"
      },
      "cell_type": "code",
      "source": [
        "#creting session and feeding in batches of training data.\n",
        "with tf.Session() as sess:\n",
        "   \n",
        "    sess.run(init)\n",
        "    for i in range(num_iter):\n",
        "        x_batch,y_batch=next_batch(train_scaled,batch_size,num_time_steps)\n",
        "        sess.run(train,feed_dict={X:x_batch,y:y_batch})\n",
        "        \n",
        "        if i%100==0:\n",
        "            mse=sess.run(loss,feed_dict={X:x_batch,y:y_batch})\n",
        "            print(\"Iteration={},lLoss={}\".format(i,mse))\n",
        "        \n",
        "    # Save Model for Later\n",
        "    saver.save(sess, \"./ex_time_series_model\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Iteration=0,lLoss=0.3555307388305664\n",
            "Iteration=100,lLoss=0.013291890732944012\n",
            "Iteration=200,lLoss=0.010796904563903809\n",
            "Iteration=300,lLoss=0.0074959187768399715\n",
            "Iteration=400,lLoss=0.009619677439332008\n",
            "Iteration=500,lLoss=0.008589799515902996\n",
            "Iteration=600,lLoss=0.005090691149234772\n",
            "Iteration=700,lLoss=0.005557559430599213\n",
            "Iteration=800,lLoss=0.008921747095882893\n",
            "Iteration=900,lLoss=0.01348074059933424\n",
            "Iteration=1000,lLoss=0.008008033968508244\n",
            "Iteration=1100,lLoss=0.004038154613226652\n",
            "Iteration=1200,lLoss=0.0017818082123994827\n",
            "Iteration=1300,lLoss=0.00570037541911006\n",
            "Iteration=1400,lLoss=0.004358663689345121\n",
            "Iteration=1500,lLoss=0.004497047048062086\n",
            "Iteration=1600,lLoss=0.0023873785976320505\n",
            "Iteration=1700,lLoss=0.0038119980599731207\n",
            "Iteration=1800,lLoss=0.0043905372731387615\n",
            "Iteration=1900,lLoss=0.004267090931534767\n",
            "Iteration=2000,lLoss=0.004509182181209326\n",
            "Iteration=2100,lLoss=0.004681126214563847\n",
            "Iteration=2200,lLoss=0.012479365803301334\n",
            "Iteration=2300,lLoss=0.008593187667429447\n",
            "Iteration=2400,lLoss=0.0043819197453558445\n",
            "Iteration=2500,lLoss=0.005066272336989641\n",
            "Iteration=2600,lLoss=0.005653595086187124\n",
            "Iteration=2700,lLoss=0.007068763021379709\n",
            "Iteration=2800,lLoss=0.007486661896109581\n",
            "Iteration=2900,lLoss=0.008170388638973236\n",
            "Iteration=3000,lLoss=0.006627346854656935\n",
            "Iteration=3100,lLoss=0.0020912550389766693\n",
            "Iteration=3200,lLoss=0.0028381720185279846\n",
            "Iteration=3300,lLoss=0.00679528946056962\n",
            "Iteration=3400,lLoss=0.007978667505085468\n",
            "Iteration=3500,lLoss=0.0024749517906457186\n",
            "Iteration=3600,lLoss=0.003934080246835947\n",
            "Iteration=3700,lLoss=0.002356875455006957\n",
            "Iteration=3800,lLoss=0.006559554021805525\n",
            "Iteration=3900,lLoss=0.0019470718689262867\n",
            "Iteration=4000,lLoss=0.005328325089067221\n",
            "Iteration=4100,lLoss=0.0018153729615733027\n",
            "Iteration=4200,lLoss=0.006771771237254143\n",
            "Iteration=4300,lLoss=0.003288921667262912\n",
            "Iteration=4400,lLoss=0.0017046350985765457\n",
            "Iteration=4500,lLoss=0.0007686528260819614\n",
            "Iteration=4600,lLoss=0.005304135847836733\n",
            "Iteration=4700,lLoss=0.001130864955484867\n",
            "Iteration=4800,lLoss=0.0022415737621486187\n",
            "Iteration=4900,lLoss=0.0059786043129861355\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "lCfbAHRiTlbL",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "______"
      ]
    },
    {
      "metadata": {
        "id": "Om5cUwQMTlbM",
        "colab_type": "code",
        "colab": {},
        "outputId": "0b217adf-688d-4e41-f7ef-798a0894e464"
      },
      "cell_type": "code",
      "source": [
        "#last 12 months of orignal dataset\n",
        "test_set"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\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>Milk Production</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1975-01-01 01:00:00</th>\n",
              "      <td>834.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-02-01 01:00:00</th>\n",
              "      <td>782.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-03-01 01:00:00</th>\n",
              "      <td>892.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-04-01 01:00:00</th>\n",
              "      <td>903.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-05-01 01:00:00</th>\n",
              "      <td>966.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-06-01 01:00:00</th>\n",
              "      <td>937.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-07-01 01:00:00</th>\n",
              "      <td>896.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-08-01 01:00:00</th>\n",
              "      <td>858.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-09-01 01:00:00</th>\n",
              "      <td>817.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-10-01 01:00:00</th>\n",
              "      <td>827.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-11-01 01:00:00</th>\n",
              "      <td>797.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-12-01 01:00:00</th>\n",
              "      <td>843.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     Milk Production\n",
              "Month                               \n",
              "1975-01-01 01:00:00            834.0\n",
              "1975-02-01 01:00:00            782.0\n",
              "1975-03-01 01:00:00            892.0\n",
              "1975-04-01 01:00:00            903.0\n",
              "1975-05-01 01:00:00            966.0\n",
              "1975-06-01 01:00:00            937.0\n",
              "1975-07-01 01:00:00            896.0\n",
              "1975-08-01 01:00:00            858.0\n",
              "1975-09-01 01:00:00            817.0\n",
              "1975-10-01 01:00:00            827.0\n",
              "1975-11-01 01:00:00            797.0\n",
              "1975-12-01 01:00:00            843.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "metadata": {
        "id": "gD2m6SWTTlbV",
        "colab_type": "code",
        "colab": {},
        "outputId": "cc950ff0-52c4-4583-fbb2-eb3b5af6c600"
      },
      "cell_type": "code",
      "source": [
        "#using our trained model to predict 12 months ahead of data\n",
        "with tf.Session() as sess:\n",
        "    \n",
        "    # Using Saver instance to restore your saved rnn time series model\n",
        "    saver.restore(sess, \"./ex_time_series_model\")\n",
        "    my_list=list(train_scaled[-12:])\n",
        "    for i in range(12):\n",
        "        x_batch=np.array(my_list[-num_time_steps:]).reshape(1,num_time_steps,1)\n",
        "        predictions=sess.run(outputs,feed_dict={X:x_batch})\n",
        "        my_list.append(predictions[0,-1,0])\n",
        "        "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Restoring parameters from ./ex_time_series_model\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "90CUElxXTlbZ",
        "colab_type": "code",
        "colab": {},
        "outputId": "7314c302-02cb-4e7b-8dda-fc9e2570f13a"
      },
      "cell_type": "code",
      "source": [
        "my_list"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([0.66105769]),\n",
              " array([0.54086538]),\n",
              " array([0.80769231]),\n",
              " array([0.83894231]),\n",
              " array([1.]),\n",
              " array([0.94711538]),\n",
              " array([0.85336538]),\n",
              " array([0.75480769]),\n",
              " array([0.62980769]),\n",
              " array([0.62259615]),\n",
              " array([0.52884615]),\n",
              " array([0.625]),\n",
              " 0.6598872,\n",
              " 0.5599241,\n",
              " 0.82050705,\n",
              " 0.85328424,\n",
              " 0.9556481,\n",
              " 0.9288611,\n",
              " 0.8025014,\n",
              " 0.72787946,\n",
              " 0.55704063,\n",
              " 0.6206851,\n",
              " 0.52133596,\n",
              " 0.5288242]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "metadata": {
        "id": "arQN--w9Tlbf",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#taking the last 12 predicted values and unscaling them to show orignal values.\n",
        "results=scaler.inverse_transform(np.array(my_list[-12:]).reshape(12,1))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "6MK_lo_4Tlbk",
        "colab_type": "code",
        "colab": {},
        "outputId": "6083c50d-0242-4927-d668-0ea88e55b351"
      },
      "cell_type": "code",
      "source": [
        "#appending our predicted values to test ses to compare results.\n",
        "test_set[\"Generated\"]=results"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "c:\\users\\asus\\appdata\\local\\programs\\python\\python36\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "z7c0HD2mTlbn",
        "colab_type": "code",
        "colab": {},
        "outputId": "f7033585-6d87-4707-faf9-9781f4c89c77"
      },
      "cell_type": "code",
      "source": [
        "test_set"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\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>Milk Production</th>\n",
              "      <th>Generated</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1975-01-01 01:00:00</th>\n",
              "      <td>834.0</td>\n",
              "      <td>827.513062</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-02-01 01:00:00</th>\n",
              "      <td>782.0</td>\n",
              "      <td>785.928406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-03-01 01:00:00</th>\n",
              "      <td>892.0</td>\n",
              "      <td>894.330933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-04-01 01:00:00</th>\n",
              "      <td>903.0</td>\n",
              "      <td>907.966248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-05-01 01:00:00</th>\n",
              "      <td>966.0</td>\n",
              "      <td>950.549622</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-06-01 01:00:00</th>\n",
              "      <td>937.0</td>\n",
              "      <td>939.406189</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-07-01 01:00:00</th>\n",
              "      <td>896.0</td>\n",
              "      <td>886.840576</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-08-01 01:00:00</th>\n",
              "      <td>858.0</td>\n",
              "      <td>855.797852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-09-01 01:00:00</th>\n",
              "      <td>817.0</td>\n",
              "      <td>784.728882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-10-01 01:00:00</th>\n",
              "      <td>827.0</td>\n",
              "      <td>811.204956</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-11-01 01:00:00</th>\n",
              "      <td>797.0</td>\n",
              "      <td>769.875732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975-12-01 01:00:00</th>\n",
              "      <td>843.0</td>\n",
              "      <td>772.990845</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     Milk Production   Generated\n",
              "Month                                           \n",
              "1975-01-01 01:00:00            834.0  827.513062\n",
              "1975-02-01 01:00:00            782.0  785.928406\n",
              "1975-03-01 01:00:00            892.0  894.330933\n",
              "1975-04-01 01:00:00            903.0  907.966248\n",
              "1975-05-01 01:00:00            966.0  950.549622\n",
              "1975-06-01 01:00:00            937.0  939.406189\n",
              "1975-07-01 01:00:00            896.0  886.840576\n",
              "1975-08-01 01:00:00            858.0  855.797852\n",
              "1975-09-01 01:00:00            817.0  784.728882\n",
              "1975-10-01 01:00:00            827.0  811.204956\n",
              "1975-11-01 01:00:00            797.0  769.875732\n",
              "1975-12-01 01:00:00            843.0  772.990845"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "metadata": {
        "id": "ORI6DVcDTlbr",
        "colab_type": "code",
        "colab": {},
        "outputId": "d8d23fbc-65b6-4b4b-9fda-320cad93d2a7"
      },
      "cell_type": "code",
      "source": [
        "#Plot out the two columns for comparison.\n",
        "test_set.plot()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x1dd648ebb70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}