Holt_Winters_Forecasting_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6DBmteGVKO34"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from pylab import rcParams\n",
    "from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes, mark_inset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "rcParams['figure.figsize'] = 14,6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FV2mAQPqIviV"
   },
   "outputs": [],
   "source": [
    "def average(series):\n",
    "    return float(sum(series))/len(series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "afn4xybsNp5l"
   },
   "outputs": [],
   "source": [
    "def moving_average(series, n):\n",
    "    return average(series[-n:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bDoYT6pKOx1D"
   },
   "outputs": [],
   "source": [
    "def weighted_average(series, weights):\n",
    "    result = 0.0\n",
    "    weights.reverse()\n",
    "    for n in range(len(weights)):\n",
    "        result += series[-n-1] * weights[n]\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UatdMrRlGjHx"
   },
   "outputs": [],
   "source": [
    "series = [3,10,12,13,12,10,12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2078,
     "status": "ok",
     "timestamp": 1558168443901,
     "user": {
      "displayName": "Bhavesh Bhatt",
      "photoUrl": "https://lh5.googleusercontent.com/-Dxy3HXzth_c/AAAAAAAAAAI/AAAAAAAAHvk/4kcnA5cyAC4/s64/photo.jpg",
      "userId": "01561702845917398436"
     },
     "user_tz": -330
    },
    "id": "k6j8mSU1H70l",
    "outputId": "0cec6ea2-4590-4718-cd6e-a87a1371592e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 10, 12, 13, 12, 10, 12, 12]\n"
     ]
    }
   ],
   "source": [
    "naive_series = series.copy()\n",
    "naive_forecast = series[-1]\n",
    "naive_series.append(naive_forecast)\n",
    "print (naive_series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 395
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1509,
     "status": "ok",
     "timestamp": 1558168471005,
     "user": {
      "displayName": "Bhavesh Bhatt",
      "photoUrl": "https://lh5.googleusercontent.com/-Dxy3HXzth_c/AAAAAAAAAAI/AAAAAAAAHvk/4kcnA5cyAC4/s64/photo.jpg",
      "userId": "01561702845917398436"
     },
     "user_tz": -330
    },
    "id": "-UgdlIYdE-OL",
    "outputId": "12af931f-2993-4f0a-9c3e-b86a6a9ca389"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1172a8b00>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(naive_series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1015,
     "status": "ok",
     "timestamp": 1558168639965,
     "user": {
      "displayName": "Bhavesh Bhatt",
      "photoUrl": "https://lh5.googleusercontent.com/-Dxy3HXzth_c/AAAAAAAAAAI/AAAAAAAAHvk/4kcnA5cyAC4/s64/photo.jpg",
      "userId": "01561702845917398436"
     },
     "user_tz": -330
    },
    "id": "YohDQ_WtJUmK",
    "outputId": "e4af4db2-663f-46c2-d2d0-02b869ed3848"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 10, 12, 13, 12, 10, 12, 10.285714285714286]\n"
     ]
    }
   ],
   "source": [
    "all_avg_series = series.copy()\n",
    "all_avg_forecast = average(series)\n",
    "all_avg_series.append(all_avg_forecast)\n",
    "print (all_avg_series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1100,
     "status": "ok",
     "timestamp": 1558168959303,
     "user": {
      "displayName": "Bhavesh Bhatt",
      "photoUrl": "https://lh5.googleusercontent.com/-Dxy3HXzth_c/AAAAAAAAAAI/AAAAAAAAHvk/4kcnA5cyAC4/s64/photo.jpg",
      "userId": "01561702845917398436"
     },
     "user_tz": -330
    },
    "id": "NoFmZGVANp91",
    "outputId": "51f4587e-6769-43dd-d1a0-8e2b3b4bcaa3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 10, 12, 13, 12, 10, 12, 11.333333333333334]\n"
     ]
    }
   ],
   "source": [
    "moving_avg_series_3 = series.copy()\n",
    "moving_avg_forecast_3 = moving_average(series, 3)\n",
    "moving_avg_series_3.append(moving_avg_forecast_3)\n",
    "print (moving_avg_series_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 830,
     "status": "ok",
     "timestamp": 1558168967548,
     "user": {
      "displayName": "Bhavesh Bhatt",
      "photoUrl": "https://lh5.googleusercontent.com/-Dxy3HXzth_c/AAAAAAAAAAI/AAAAAAAAHvk/4kcnA5cyAC4/s64/photo.jpg",
      "userId": "01561702845917398436"
     },
     "user_tz": -330
    },
    "id": "NfYS7sZXN9SO",
    "outputId": "1231ad94-38a6-49f8-a293-90d5f3244bd1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 10, 12, 13, 12, 10, 12, 11.75]\n"
     ]
    }
   ],
   "source": [
    "moving_avg_series_4 = series.copy()\n",
    "moving_avg_forecast_4 = moving_average(series, 4)\n",
    "moving_avg_series_4.append(moving_avg_forecast_4)\n",
    "print (moving_avg_series_4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1357,
     "status": "ok",
     "timestamp": 1558169305542,
     "user": {
      "displayName": "Bhavesh Bhatt",
      "photoUrl": "https://lh5.googleusercontent.com/-Dxy3HXzth_c/AAAAAAAAAAI/AAAAAAAAHvk/4kcnA5cyAC4/s64/photo.jpg",
      "userId": "01561702845917398436"
     },
     "user_tz": -330
    },
    "id": "_PELEiyBPJDE",
    "outputId": "ea6ba8df-2afe-4f76-9739-22b001d7c59d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 10, 12, 13, 12, 10, 12, 11.500000000000002]\n"
     ]
    }
   ],
   "source": [
    "#Weights should add up to 1 or else you get weird predictions\n",
    "weighted_avg_series = series.copy()\n",
    "weights = [0.1, 0.2, 0.3, 0.4]\n",
    "weighted_avg_forecast = weighted_average(series, weights)\n",
    "weighted_avg_series.append(weighted_avg_forecast)\n",
    "print (weighted_avg_series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1177,
     "status": "ok",
     "timestamp": 1558169259443,
     "user": {
      "displayName": "Bhavesh Bhatt",
      "photoUrl": "https://lh5.googleusercontent.com/-Dxy3HXzth_c/AAAAAAAAAAI/AAAAAAAAHvk/4kcnA5cyAC4/s64/photo.jpg",
      "userId": "01561702845917398436"
     },
     "user_tz": -330
    },
    "id": "l98F700JH9Cn",
    "outputId": "ecf5caa5-72ab-4ad1-956b-7ad4cb892116"
   },
   "outputs": [],
   "source": [
    "#Weights should add up to 1 or else you get weird predictions\n",
    "# weighted_avg_series = series.copy()\n",
    "# weights = [1.1, 1.2, 1.3, 1.4]\n",
    "# weighted_avg_forecast = weighted_average(series, weights)\n",
    "# weighted_avg_series.append(weighted_avg_forecast)\n",
    "# print (weighted_avg_series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 395
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3095,
     "status": "ok",
     "timestamp": 1558169321304,
     "user": {
      "displayName": "Bhavesh Bhatt",
      "photoUrl": "https://lh5.googleusercontent.com/-Dxy3HXzth_c/AAAAAAAAAAI/AAAAAAAAHvk/4kcnA5cyAC4/s64/photo.jpg",
      "userId": "01561702845917398436"
     },
     "user_tz": -330
    },
    "id": "JBm7UX8AKJXO",
    "outputId": "e0aaef23-e6cf-4881-c196-122f9d5ed33b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11741df60>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(all_avg_series, color='blue', linestyle='dashed', label='All Average Series')\n",
    "plt.plot(naive_series, color='green', linestyle='dashed', label='Naive Series')\n",
    "plt.plot(moving_avg_series_4, color='cyan', linestyle='dashed', label='3 Moving Average')\n",
    "plt.plot(moving_avg_series_3, color='magenta', linestyle='dashed', label='4 Moving Average')\n",
    "plt.plot(weighted_avg_series, color='red', linestyle='dashed', label='Weighted Average')\n",
    "plt.plot(series, color='black', linewidth=2)\n",
    "plt.legend(loc='lower right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 378
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3734,
     "status": "ok",
     "timestamp": 1558169329475,
     "user": {
      "displayName": "Bhavesh Bhatt",
      "photoUrl": "https://lh5.googleusercontent.com/-Dxy3HXzth_c/AAAAAAAAAAI/AAAAAAAAHvk/4kcnA5cyAC4/s64/photo.jpg",
      "userId": "01561702845917398436"
     },
     "user_tz": -330
    },
    "id": "i3vElScnKZlt",
    "outputId": "fee97123-6443-4dab-8274-fa5c16fd6614"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(all_avg_series, color='blue', linestyle='dashed', label='All Average Series')\n",
    "plt.plot(naive_series, color='green', linestyle='dashed', label='Naive Series')\n",
    "plt.plot(moving_avg_series_4, color='cyan', linestyle='dashed', label='3 Moving Average')\n",
    "plt.plot(moving_avg_series_3, color='magenta', linestyle='dashed', label='4 Moving Average')\n",
    "plt.plot(weighted_avg_series, color='red', linestyle='dashed', label='Weighted Average')\n",
    "plt.plot(series, color='black', linewidth=2)\n",
    "plt.legend(loc='lower right')\n",
    "plt.axis([5,8,8,13])\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Holt_Winters_Forecasting_1.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}