Holt_Winters_Forecasting_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "rcParams['figure.figsize'] = 14,6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UatdMrRlGjHx"
   },
   "outputs": [],
   "source": [
    "series = [3,10,12,13,12,10,12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1u4mLzOSVaAv"
   },
   "outputs": [],
   "source": [
    "def exponential_smoothing(series, alpha):\n",
    "    result = [series[0]] # first value is same as series\n",
    "    for n in range(1, len(series)):\n",
    "        result.append(alpha * series[n] + (1 - alpha) * result[n-1])\n",
    "        print (result)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 138
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1142,
     "status": "ok",
     "timestamp": 1558170198098,
     "user": {
      "displayName": "Bhavesh Bhatt",
      "photoUrl": "https://lh5.googleusercontent.com/-Dxy3HXzth_c/AAAAAAAAAAI/AAAAAAAAHvk/4kcnA5cyAC4/s64/photo.jpg",
      "userId": "01561702845917398436"
     },
     "user_tz": -330
    },
    "id": "q36Df31BWi-0",
    "outputId": "a3019f94-49f0-4cc3-f80f-116eddf3f38f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 9.3]\n",
      "[3, 9.3, 11.73]\n",
      "[3, 9.3, 11.73, 12.873000000000001]\n",
      "[3, 9.3, 11.73, 12.873000000000001, 12.0873]\n",
      "[3, 9.3, 11.73, 12.873000000000001, 12.0873, 10.20873]\n",
      "[3, 9.3, 11.73, 12.873000000000001, 12.0873, 10.20873, 11.820873]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[3, 9.3, 11.73, 12.873000000000001, 12.0873, 10.20873, 11.820873]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exponential_smoothing(series, 0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 138
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1567,
     "status": "ok",
     "timestamp": 1558170313188,
     "user": {
      "displayName": "Bhavesh Bhatt",
      "photoUrl": "https://lh5.googleusercontent.com/-Dxy3HXzth_c/AAAAAAAAAAI/AAAAAAAAHvk/4kcnA5cyAC4/s64/photo.jpg",
      "userId": "01561702845917398436"
     },
     "user_tz": -330
    },
    "id": "aKHcqvI6W3ad",
    "outputId": "0b99bf24-acfc-4d63-ab18-e510c1fe8431"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 3.7]\n",
      "[3, 3.7, 4.53]\n",
      "[3, 3.7, 4.53, 5.377]\n",
      "[3, 3.7, 4.53, 5.377, 6.0393]\n",
      "[3, 3.7, 4.53, 5.377, 6.0393, 6.43537]\n",
      "[3, 3.7, 4.53, 5.377, 6.0393, 6.43537, 6.991833]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[3, 3.7, 4.53, 5.377, 6.0393, 6.43537, 6.991833]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exponential_smoothing(series, 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 224
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2393,
     "status": "ok",
     "timestamp": 1558170374302,
     "user": {
      "displayName": "Bhavesh Bhatt",
      "photoUrl": "https://lh5.googleusercontent.com/-Dxy3HXzth_c/AAAAAAAAAAI/AAAAAAAAHvk/4kcnA5cyAC4/s64/photo.jpg",
      "userId": "01561702845917398436"
     },
     "user_tz": -330
    },
    "id": "T_oE_MjxVtIE",
    "outputId": "66866385-c2b6-47ef-e42f-3f0b3ac1004a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 3.7]\n",
      "[3, 3.7, 4.53]\n",
      "[3, 3.7, 4.53, 5.377]\n",
      "[3, 3.7, 4.53, 5.377, 6.0393]\n",
      "[3, 3.7, 4.53, 5.377, 6.0393, 6.43537]\n",
      "[3, 3.7, 4.53, 5.377, 6.0393, 6.43537, 6.991833]\n",
      "[3, 9.3]\n",
      "[3, 9.3, 11.73]\n",
      "[3, 9.3, 11.73, 12.873000000000001]\n",
      "[3, 9.3, 11.73, 12.873000000000001, 12.0873]\n",
      "[3, 9.3, 11.73, 12.873000000000001, 12.0873, 10.20873]\n",
      "[3, 9.3, 11.73, 12.873000000000001, 12.0873, 10.20873, 11.820873]\n"
     ]
    }
   ],
   "source": [
    "series_0_point_1 = exponential_smoothing(series, 0.1)\n",
    "series_0_point_9 = exponential_smoothing(series, 0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 395
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4255,
     "status": "ok",
     "timestamp": 1558170378407,
     "user": {
      "displayName": "Bhavesh Bhatt",
      "photoUrl": "https://lh5.googleusercontent.com/-Dxy3HXzth_c/AAAAAAAAAAI/AAAAAAAAHvk/4kcnA5cyAC4/s64/photo.jpg",
      "userId": "01561702845917398436"
     },
     "user_tz": -330
    },
    "id": "aziQhutbVtSU",
    "outputId": "ae2b43f7-7bd1-4b72-c49a-2928944def94"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1177a3898>"
      ]
     },
     "execution_count": 8,
     "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(series, color='black', linewidth=2, label='Original Series')\n",
    "plt.plot(series_0_point_1, color='red', linewidth=2, label='0.1 Alpha Series')\n",
    "plt.plot(series_0_point_9, color='blue', linewidth=2, label='0.9 Alpha Series')\n",
    "plt.legend(loc='lower right')"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Holt_Winters_Forecasting_2.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
}