assignment forecsting on the airlines data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt\n",
    "from pandas.plotting import lag_plot\n",
    "from sklearn.model_selection import train_test_split\n",
    "import warnings\n",
    "import itertools\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "from statsmodels.tsa.holtwinters import SimpleExpSmoothing\n",
    "from statsmodels.tsa.holtwinters import Holt\n",
    "from statsmodels.tsa.holtwinters import ExponentialSmoothing\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "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>Month</th>\n",
       "      <th>Passengers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1995-01-01</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1995-02-01</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1995-03-01</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1995-04-01</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1995-05-01</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>2002-08-01</td>\n",
       "      <td>405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>2002-09-01</td>\n",
       "      <td>355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>2002-10-01</td>\n",
       "      <td>306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>2002-11-01</td>\n",
       "      <td>271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>2002-12-01</td>\n",
       "      <td>306</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Month  Passengers\n",
       "0  1995-01-01         112\n",
       "1  1995-02-01         118\n",
       "2  1995-03-01         132\n",
       "3  1995-04-01         129\n",
       "4  1995-05-01         121\n",
       "..        ...         ...\n",
       "91 2002-08-01         405\n",
       "92 2002-09-01         355\n",
       "93 2002-10-01         306\n",
       "94 2002-11-01         271\n",
       "95 2002-12-01         306\n",
       "\n",
       "[96 rows x 2 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_excel('Airlines+Data.xlsx')\n",
    "data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# performing the basic EDA analysis on the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 96 entries, 0 to 95\n",
      "Data columns (total 2 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   Month       96 non-null     datetime64[ns]\n",
      " 1   Passengers  96 non-null     int64         \n",
      "dtypes: datetime64[ns](1), int64(1)\n",
      "memory usage: 1.6 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "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>Passengers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>96.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>213.708333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>71.918216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>104.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>156.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>264.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>413.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Passengers\n",
       "count   96.000000\n",
       "mean   213.708333\n",
       "std     71.918216\n",
       "min    104.000000\n",
       "25%    156.000000\n",
       "50%    200.000000\n",
       "75%    264.750000\n",
       "max    413.000000"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month         0\n",
       "Passengers    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# performing the basic visualization of the forecasting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_vis=pd.read_excel('Airlines+Data.xlsx',header=0,index_col=0,parse_dates=True,squeeze=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Month'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_vis.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([13., 14., 19.,  9., 14., 12.,  9.,  1.,  3.,  2.]),\n",
       " array([104. , 134.9, 165.8, 196.7, 227.6, 258.5, 289.4, 320.3, 351.2,\n",
       "        382.1, 413. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data_vis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_vis.plot(kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mcY+IbwE/K3i8xcCaiDgYEU8Ce4BzOshnZmbj0Emf+wckvQ/YBHwoIn4OzAEeathmb2obQdJSYClAX18fQ0NDHUQZaXh4uPRjtmPZhud5vuAkR0U/1JwxHT53wYwOUnVmMl/Poib7370o5yxPFTLCJOSMiDFvwDxge8P9PmAa9TP/lcBtqf1zwHsbtrsVeM9Yx+/v74+y1Wq10o/ZjrnL1xfarp2cRY/ZDZP52O2Y7H/3opyzPFXIGNGdnMCmaFFXx3XmHhE/Orws6V+A9enuXuCUhk1PBn44nsdoR69dbdHMbLKN60tMkmY33H03cHgkzTpgiaSjJM0HFgAbO4s4tmbvWnOXr2/1V4iZWfbGPHOXdDuwCJglaS+wAlgk6UwggKeA9wNExA5Ja4GdwIvAsoh4qSvJrWs8CYZZ9Y1Z3CPi8ibNt46y/Urq/fBWUc0KtifBMKsWX1vGzCxDLu5mZhnytWW6pK35SQvMTVo/JkzU/KRmVm0u7l1SdH7SdvqyPT+pmRVVueJ+xvX3sf+FYl/9LFoMjz16OltXXNhJLDOznlK54r7/hUM+IzYzG4M/UDUzy5CLu5lZhlzczcwy5OJuZpahyn2gWqXx44U/qC0w8TR48mkzK65yxb0q48eLTibtiafNrBvcLWNmliEXdzOzDLm4m5llqHJ97uAPKs3MxlK54u4PKs3MxuZuGTOzDFXuzL3KPDepmU0Un7lPoIgYcavVak3bzcw64eJuZpYhF3czswy5uJuZZWjM4i7pNknPSdre0HaCpPslPZF+Ht+wblDSHkm7JV3UreBmZtZakTP3LwEXv6JtANgQEQuADek+khYCS4DT0j43S5pWWtoWJI24/eDGdzZtNzObCsYs7hHxLeBnr2hezMsX1F0NvKuhfU1EHIyIJ4E9wDnlRB01o0ehmJk1UJGCJ2kesD4i3pTu/yIijmtY//OIOF7SZ4GHIuIrqf1W4JsR8R9NjrkUWArQ19fXv2bNmhKezsuGh4c55phjSj1mNzhnuZyzXFXIWYWM0J2c559//uaIOLvZurK/xNSs36Ppu0dE3ALcAnD22WdH0WuvF9XO9dwnk3OWyznLVYWcVcgIE59zvKNlfiRpNkD6+Vxq3wuc0rDdycAPxx/PzMzGY7zFfR1wZVq+ErizoX2JpKMkzQcWABs7i2hmZu0as1tG0u3AImCWpL3ACmAVsFbS1cDTwGUAEbFD0lpgJ/AisCwiXupSdjMza2HM4h4Rl7dYdUGL7VcCKzsJZWZmnfE3VM3MMuTibmaWoULj3LseQvox8IOSDzsL+EnJx+wG5yyXc5arCjmrkBG6k3NuRJzYbEVPFPdukLSp1eD+XuKc5XLOclUhZxUywsTndLeMmVmGXNzNzDKUc3G/ZbIDFOSc5XLOclUhZxUywgTnzLbP3cxsKsv5zN3MbMpycTczy1ClinuLKf/OkPSgpEcl3SXptan9VZK+mNq3SlrUsM9QmgZwS7qd1IMZXyXpFkmPS3pM0nvKylhWTkkzG17DLZJ+IummXsuZ1l2e2rdJukfSrB7N+Wcp4w5JHy8zYzr+KZJqknalx7gmtbc9daak/vQc9kj6jFTOVGclZ1wp6RlJw2Vk60ZOSa+RdHf6Pd8haVUpAZvNVtSrN+CtwJuB7Q1t/wP8UVq+CrghLS8DvpiWTwI2A0ek+0PA2T2e8Xrgo2n5CGBWL+Z8xTE3A2/ttZzUr6H03OHXEPg4cF0P5nwd9QvxnZjWrQYuKDnnbODNaXkm8DiwML0mA6l9ALgxLS8EtgJHAfOB7wHT0rqNwHnU53H4JvDHPZjx3HS84TJfxzJzAq8Bzk/bvAr4dhmvZaXO3KP5lH+/D3wrLd8PHD7DXUh9flci4jngF0DXv0BQYsargI+ldb+JiFK/2Vb2aylpAfVC9e0ezKl0m5HOLl9LyfMMlJTzd4HHI+LHabsHGvYpK+e+iHgkLR8AdgFzaHPqTNXncXhtRDwY9ar05YZ9eiJj2v+hiNhXRq5u5YyIX0VELR3n18Aj1OfC6EilinsL24E/TcuX8fJkIVuBxZKOVP3a8v389kQiX0xdCf9Y1p+TZWWUdFxaf4OkRyR9TVJflzO2nfMV+14OfDX9ovdUzog4BPw18Cj1or4QuLXXclL/ZX+jpHmSjqReFF75OpdG9ekzzwIeBvoOF8H083BX5RzgmYbd9qa2OWn5le29lHHClJUz/e7/CenNvxM5FPergGWSNlP/0+jXqf026i/eJuAm4LvUrzEP8OcRcTrwh+l2RY9lPJL6O/d/R8SbgQeBT3Q543hyNloC3D4BGaHNnJKmUy/uZwGvB7YBg72WMyJ+nnJ+lfpfQE8x8nUuhaRjgDuAayPil6Nt2qQtRmkvTQkZJ0RZOdMb+u3AZyLi+53mKnsO1QkXEY8BFwJIegNwaWp/Efjbw9tJ+i7wRFr3bPp5QNK/U/8T7ss9lPGnwK+Ar6dVXwOu7la+DnIevn8GcGREbO52xnHmPDOt/15qX0u9L7TXchIRdwF3pfalQOmT3aQ3uzuAf4uI/0zNP5I0OyL2qdjUmXv57a6DUqfULClj15Wc8xbgiYi4qYxslT9zVxrpIukI4B+Af073XyNpRlp+B/Uzo53pT+FZqX068E7qfz73TMbUtXEX9RmwoD4xys5uZhxPzoZdL2fiztrHk/NZYKGkw1fPewf1/tFey9m4z/HA3wBfKDmTqHdJ7YqITzWsamvqzNTdcEDSuemY72vYpycylpFlonJK+ihwLHBtaQHL/gS5mzfqBWQfcIj6u+DVwDXUP6V+nPr0f4e/dTsP2E39l/gB6pfGBJhBfXTCNmAH8E+kT9Z7JWNaN5f6h3HbqPe//U6vvZYNx/o+8MZe/TdP6/4qtW+j/sb5uh7NeTv1N/KdwJIuvJ5/QL0rYBuwJd0uoT5SZwP1vyA2ACc07PP31Ed27KZhFAf1D4G3p3WfPfz8eizjx9O/x2/Sz+t67bWkfgYf6f/D4eP8Zaf5fPkBM7MMVb5bxszMRnJxNzPLkIu7mVmGXNzNzDLk4m5mliEXdzOzDLm4m5ll6P8Bk6GhBkWmx4gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "group=data_vis.groupby(pd.Grouper(freq='A'))\n",
    "years=pd.DataFrame()\n",
    "for i,j in group:\n",
    "    years[i.year]=j.values\n",
    "years.boxplot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='y(t)', ylabel='y(t + 1)'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lag_plot(data_vis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# performing the data driven methods on the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=data.head(80)# spliting the data into train and test\n",
    "test=data.tail(16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# moving average  and time decomposition plot  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e5fda65f10>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "data.Passengers.plot(label='org')\n",
    "for i in range(2,18,4):\n",
    "    data['Passengers'].rolling(i).mean().plot(label=(str(i)))\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-94-1206836dc663>:1: FutureWarning: the 'freq'' keyword is deprecated, use 'period' instead\n",
      "  time_plot=seasonal_decompose(data.Passengers,freq=2)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "time_plot=seasonal_decompose(data.Passengers,freq=2)\n",
    "time_plot.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# creating my metric MAPE inorder to find the model performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Mape(pred,org):\n",
    "    temp=np.abs((pred-org)/org)*100\n",
    "    return np.mean(temp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# developing the model of simple exponential  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-57-10ade0cc1a74>: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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  train['Passengers']=train['Passengers'].astype('double')\n",
      "C:\\Users\\ELCOT\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\holtwinters\\model.py:427: FutureWarning: After 0.13 initialization must be handled at model creation\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "train['Passengers']=train['Passengers'].astype('double')\n",
    "sem_model=SimpleExpSmoothing(train['Passengers']).fit(smoothing_level=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80    297.434175\n",
       "81    297.434175\n",
       "82    297.434175\n",
       "83    297.434175\n",
       "84    297.434175\n",
       "85    297.434175\n",
       "86    297.434175\n",
       "87    297.434175\n",
       "88    297.434175\n",
       "89    297.434175\n",
       "90    297.434175\n",
       "91    297.434175\n",
       "92    297.434175\n",
       "93    297.434175\n",
       "94    297.434175\n",
       "95    297.434175\n",
       "dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_sem=sem_model.predict(start=test.index[0],end=test.index[-1])\n",
    "pred_sem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.375850554268187"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sem_mape=Mape(pred_sem,test.Passengers)\n",
    "sem_mape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# developing the holt method "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ELCOT\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\holtwinters\\model.py:427: FutureWarning: After 0.13 initialization must be handled at model creation\n",
      "  warnings.warn(\n",
      "<ipython-input-62-419ec31a46f8>:1: FutureWarning: the 'smoothing_slope'' keyword is deprecated, use 'smoothing_trend' instead\n",
      "  holt_method=Holt(train['Passengers']).fit(smoothing_level=0.8,smoothing_slope=0.2)\n"
     ]
    }
   ],
   "source": [
    "holt_method=Holt(train['Passengers']).fit(smoothing_level=0.8,smoothing_slope=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80    366.386458\n",
       "81    380.542978\n",
       "82    394.699497\n",
       "83    408.856016\n",
       "84    423.012535\n",
       "85    437.169055\n",
       "86    451.325574\n",
       "87    465.482093\n",
       "88    479.638612\n",
       "89    493.795132\n",
       "90    507.951651\n",
       "91    522.108170\n",
       "92    536.264689\n",
       "93    550.421208\n",
       "94    564.577728\n",
       "95    578.734247\n",
       "dtype: float64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "holt_pred=holt_method.predict(start=test.index[0],end=test.index[-1])\n",
    "holt_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51.93443984363068"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "holt_mape=Mape(holt_pred,test.Passengers)\n",
    "holt_mape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# holt  winter exponential smoothing with additive seasonality and additive trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ELCOT\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\holtwinters\\model.py:427: FutureWarning: After 0.13 initialization must be handled at model creation\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "holt_win_model = ExponentialSmoothing(train['Passengers'],seasonal=\"add\",trend=\"add\",seasonal_periods=12).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80    323.499100\n",
       "81    300.500131\n",
       "82    279.499613\n",
       "83    300.997600\n",
       "84    305.163478\n",
       "85    306.021326\n",
       "86    333.306583\n",
       "87    327.165831\n",
       "88    326.450361\n",
       "89    350.451481\n",
       "90    375.452321\n",
       "91    374.594423\n",
       "92    351.093523\n",
       "93    328.094554\n",
       "94    307.094036\n",
       "95    328.592023\n",
       "dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "holt_win_model_pred=holt_win_model.predict(start=test.index[0],end=test.index[-1])\n",
    "holt_win_model_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.60851728441626"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "holt_win_model_mape=Mape(holt_win_model_pred,test['Passengers'])\n",
    "holt_win_model_mape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Holts winter exponential smoothing with multiplicative seasonality and additive trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ELCOT\\anaconda3\\lib\\site-packages\\statsmodels\\tsa\\holtwinters\\model.py:427: FutureWarning: After 0.13 initialization must be handled at model creation\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "holt_win_model2 = ExponentialSmoothing(train['Passengers'],seasonal=\"mul\",trend=\"add\",seasonal_periods=12).fit() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80    310.167059\n",
       "81    273.377820\n",
       "82    239.337849\n",
       "83    270.781202\n",
       "84    276.719778\n",
       "85    274.057299\n",
       "86    317.640605\n",
       "87    307.795642\n",
       "88    306.484210\n",
       "89    343.878921\n",
       "90    384.063754\n",
       "91    379.425829\n",
       "92    336.658995\n",
       "93    296.562499\n",
       "94    259.493215\n",
       "95    293.425594\n",
       "dtype: float64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "holt_win_model2_pred=holt_win_model2.predict(start=test.index[0],end=test.index[-1])\n",
    "holt_win_model2_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.2181442853689104"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "holt_win_model2_mape=Mape(holt_win_model2_pred,test['Passengers'])\n",
    "holt_win_model2_mape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# from the above model we are using the holts winter exponential smoothing with multiplicative seasonality and additive trend because it has given the lowest value of the Mape  and applying on the entire data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Passengers']=data['Passengers'].astype('double')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=ExponentialSmoothing(data['Passengers'],seasonal=\"mul\",trend=\"add\",seasonal_periods=12).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "96     312.890018\n",
       "97     308.161373\n",
       "98     355.534360\n",
       "99     345.759644\n",
       "100    345.685315\n",
       "dtype: float64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.forecast(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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