Forecasting Facebook FbProphet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Forecasting Timeseries Data Using Facebook FbProphet\n",
    "\n",
    "Steps Required With FbProphet\n",
    "1. Introduction And Installation\n",
    "2. Data Preprocessing With Time Seires\n",
    "3. Model Fitting\n",
    "4. Obtaining The Forecasts\n",
    "5. Plotting The Forecasts\n",
    "6. Cross Validation\n",
    "7. Computing Performance Metrics\n",
    "8. Visualize the Performance MEtrics\n",
    "9. Conclusions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "### pip install pystan\n",
    "### conda install -c conda-forge fbprophet\n",
    "import pandas as pd\n",
    "import fbprophet\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Monthly milk production: pounds per cow. Jan 62 ? Dec 75</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1962-01</td>\n",
       "      <td>589.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1962-02</td>\n",
       "      <td>561.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1962-03</td>\n",
       "      <td>640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1962-04</td>\n",
       "      <td>656.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1962-05</td>\n",
       "      <td>727.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Month  Monthly milk production: pounds per cow. Jan 62 ? Dec 75\n",
       "0  1962-01                                              589.0       \n",
       "1  1962-02                                              561.0       \n",
       "2  1962-03                                              640.0       \n",
       "3  1962-04                                              656.0       \n",
       "4  1962-05                                              727.0       "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('monthly-milk-production-pounds.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Monthly milk production: pounds per cow. Jan 62 ? Dec 75</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>1975-09</td>\n",
       "      <td>817.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>1975-10</td>\n",
       "      <td>827.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>1975-11</td>\n",
       "      <td>797.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>1975-12</td>\n",
       "      <td>843.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>Monthly milk production: pounds per cow. Jan 6...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Month  \\\n",
       "164                                            1975-09   \n",
       "165                                            1975-10   \n",
       "166                                            1975-11   \n",
       "167                                            1975-12   \n",
       "168  Monthly milk production: pounds per cow. Jan 6...   \n",
       "\n",
       "     Monthly milk production: pounds per cow. Jan 62 ? Dec 75  \n",
       "164                                              817.0         \n",
       "165                                              827.0         \n",
       "166                                              797.0         \n",
       "167                                              843.0         \n",
       "168                                                NaN         "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(168,axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Monthly milk production: pounds per cow. Jan 62 ? Dec 75</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>1975-08</td>\n",
       "      <td>858.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>1975-09</td>\n",
       "      <td>817.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>1975-10</td>\n",
       "      <td>827.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>1975-11</td>\n",
       "      <td>797.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>1975-12</td>\n",
       "      <td>843.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       Month  Monthly milk production: pounds per cow. Jan 62 ? Dec 75\n",
       "163  1975-08                                              858.0       \n",
       "164  1975-09                                              817.0       \n",
       "165  1975-10                                              827.0       \n",
       "166  1975-11                                              797.0       \n",
       "167  1975-12                                              843.0       "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns=[\"ds\",\"y\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x284800b26a0>"
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     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1962-01</td>\n",
       "      <td>589.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1962-02</td>\n",
       "      <td>561.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1962-03</td>\n",
       "      <td>640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1962-04</td>\n",
       "      <td>656.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1962-05</td>\n",
       "      <td>727.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ds      y\n",
       "0  1962-01  589.0\n",
       "1  1962-02  561.0\n",
       "2  1962-03  640.0\n",
       "3  1962-04  656.0\n",
       "4  1962-05  727.0"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ds']=pd.to_datetime(df['ds'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1962-01-01</td>\n",
       "      <td>589.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1962-02-01</td>\n",
       "      <td>561.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1962-03-01</td>\n",
       "      <td>640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1962-04-01</td>\n",
       "      <td>656.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1962-05-01</td>\n",
       "      <td>727.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ds      y\n",
       "0 1962-01-01  589.0\n",
       "1 1962-02-01  561.0\n",
       "2 1962-03-01  640.0\n",
       "3 1962-04-01  656.0\n",
       "4 1962-05-01  727.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x284800b2390>"
      ]
     },
     "execution_count": 66,
     "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": [
    "df['y'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1962-01-01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1962-02-01</td>\n",
       "      <td>-28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1962-03-01</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1962-04-01</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1962-05-01</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ds     y\n",
       "0 1962-01-01   NaN\n",
       "1 1962-02-01 -28.0\n",
       "2 1962-03-01  79.0\n",
       "3 1962-04-01  16.0\n",
       "4 1962-05-01  71.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x284800ef748>"
      ]
     },
     "execution_count": 67,
     "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": [
    "df['y'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fbprophet import Prophet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__class__',\n",
       " '__delattr__',\n",
       " '__dict__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__le__',\n",
       " '__lt__',\n",
       " '__module__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__setattr__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " '__weakref__',\n",
       " '_load_stan_backend',\n",
       " 'add_country_holidays',\n",
       " 'add_group_component',\n",
       " 'add_regressor',\n",
       " 'add_seasonality',\n",
       " 'construct_holiday_dataframe',\n",
       " 'fit',\n",
       " 'fourier_series',\n",
       " 'initialize_scales',\n",
       " 'linear_growth_init',\n",
       " 'logistic_growth_init',\n",
       " 'make_all_seasonality_features',\n",
       " 'make_future_dataframe',\n",
       " 'make_holiday_features',\n",
       " 'make_seasonality_features',\n",
       " 'parse_seasonality_args',\n",
       " 'percentile',\n",
       " 'piecewise_linear',\n",
       " 'piecewise_logistic',\n",
       " 'plot',\n",
       " 'plot_components',\n",
       " 'predict',\n",
       " 'predict_seasonal_components',\n",
       " 'predict_trend',\n",
       " 'predict_uncertainty',\n",
       " 'predictive_samples',\n",
       " 'regressor_column_matrix',\n",
       " 'sample_model',\n",
       " 'sample_posterior_predictive',\n",
       " 'sample_predictive_trend',\n",
       " 'set_auto_seasonalities',\n",
       " 'set_changepoints',\n",
       " 'setup_dataframe',\n",
       " 'validate_column_name',\n",
       " 'validate_inputs']"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(Prophet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1962-01-01</td>\n",
       "      <td>589.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1962-02-01</td>\n",
       "      <td>561.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1962-03-01</td>\n",
       "      <td>640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1962-04-01</td>\n",
       "      <td>656.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1962-05-01</td>\n",
       "      <td>727.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ds      y\n",
       "0 1962-01-01  589.0\n",
       "1 1962-02-01  561.0\n",
       "2 1962-03-01  640.0\n",
       "3 1962-04-01  656.0\n",
       "4 1962-05-01  727.0"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
      "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<fbprophet.forecaster.Prophet at 0x28480124828>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### intiialize the Model\n",
    "model=Prophet()\n",
    "model.fit(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<fbprophet.forecaster.Prophet at 0x28480124828>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('yearly',\n",
       "              {'period': 365.25,\n",
       "               'fourier_order': 10,\n",
       "               'prior_scale': 10.0,\n",
       "               'mode': 'additive',\n",
       "               'condition_name': None})])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.seasonalities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'additive': ['yearly',\n",
       "  'additive_terms',\n",
       "  'extra_regressors_additive',\n",
       "  'holidays'],\n",
       " 'multiplicative': ['multiplicative_terms', 'extra_regressors_multiplicative']}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.component_modes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Create future dates of 365 days\n",
    "future_dates=model.make_future_dataframe(periods=365)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>1975-08-01</td>\n",
       "      <td>858.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>1975-09-01</td>\n",
       "      <td>817.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>1975-10-01</td>\n",
       "      <td>827.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>1975-11-01</td>\n",
       "      <td>797.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>1975-12-01</td>\n",
       "      <td>843.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ds      y\n",
       "163 1975-08-01  858.0\n",
       "164 1975-09-01  817.0\n",
       "165 1975-10-01  827.0\n",
       "166 1975-11-01  797.0\n",
       "167 1975-12-01  843.0"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>1962-01-01</td>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
       "      <td>1962-03-01</td>\n",
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
       "      <td>1962-05-01</td>\n",
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       "      <th>5</th>\n",
       "      <td>1962-06-01</td>\n",
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       "      <th>6</th>\n",
       "      <td>1962-07-01</td>\n",
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       "      <th>7</th>\n",
       "      <td>1962-08-01</td>\n",
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       "      <th>8</th>\n",
       "      <td>1962-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1962-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1962-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1962-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1963-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1963-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1963-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1963-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1963-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1963-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1963-07-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1963-08-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1963-09-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1963-10-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1963-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1963-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1964-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1964-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1964-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1964-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1964-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1964-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>1976-11-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>1976-11-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>1976-11-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>1976-11-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>1976-11-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>1976-11-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>1976-11-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>1976-11-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>1976-11-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>1976-11-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>1976-11-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>1976-11-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>1976-11-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>1976-11-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>517</th>\n",
       "      <td>1976-11-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>518</th>\n",
       "      <td>1976-11-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519</th>\n",
       "      <td>1976-11-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>520</th>\n",
       "      <td>1976-11-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>1976-11-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>1976-11-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>1976-11-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>1976-11-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>1976-11-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>526</th>\n",
       "      <td>1976-11-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>1976-11-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>1976-11-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>1976-11-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>1976-11-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>1976-11-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532</th>\n",
       "      <td>1976-11-30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>533 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ds\n",
       "0   1962-01-01\n",
       "1   1962-02-01\n",
       "2   1962-03-01\n",
       "3   1962-04-01\n",
       "4   1962-05-01\n",
       "5   1962-06-01\n",
       "6   1962-07-01\n",
       "7   1962-08-01\n",
       "8   1962-09-01\n",
       "9   1962-10-01\n",
       "10  1962-11-01\n",
       "11  1962-12-01\n",
       "12  1963-01-01\n",
       "13  1963-02-01\n",
       "14  1963-03-01\n",
       "15  1963-04-01\n",
       "16  1963-05-01\n",
       "17  1963-06-01\n",
       "18  1963-07-01\n",
       "19  1963-08-01\n",
       "20  1963-09-01\n",
       "21  1963-10-01\n",
       "22  1963-11-01\n",
       "23  1963-12-01\n",
       "24  1964-01-01\n",
       "25  1964-02-01\n",
       "26  1964-03-01\n",
       "27  1964-04-01\n",
       "28  1964-05-01\n",
       "29  1964-06-01\n",
       "..         ...\n",
       "503 1976-11-01\n",
       "504 1976-11-02\n",
       "505 1976-11-03\n",
       "506 1976-11-04\n",
       "507 1976-11-05\n",
       "508 1976-11-06\n",
       "509 1976-11-07\n",
       "510 1976-11-08\n",
       "511 1976-11-09\n",
       "512 1976-11-10\n",
       "513 1976-11-11\n",
       "514 1976-11-12\n",
       "515 1976-11-13\n",
       "516 1976-11-14\n",
       "517 1976-11-15\n",
       "518 1976-11-16\n",
       "519 1976-11-17\n",
       "520 1976-11-18\n",
       "521 1976-11-19\n",
       "522 1976-11-20\n",
       "523 1976-11-21\n",
       "524 1976-11-22\n",
       "525 1976-11-23\n",
       "526 1976-11-24\n",
       "527 1976-11-25\n",
       "528 1976-11-26\n",
       "529 1976-11-27\n",
       "530 1976-11-28\n",
       "531 1976-11-29\n",
       "532 1976-11-30\n",
       "\n",
       "[533 rows x 1 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "future_dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Prediction\n",
    "prediction=model.predict(future_dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1962-01-01</td>\n",
       "      <td>606.810747</td>\n",
       "      <td>577.139269</td>\n",
       "      <td>596.568861</td>\n",
       "      <td>606.810747</td>\n",
       "      <td>606.810747</td>\n",
       "      <td>-20.113596</td>\n",
       "      <td>-20.113596</td>\n",
       "      <td>-20.113596</td>\n",
       "      <td>-20.113596</td>\n",
       "      <td>-20.113596</td>\n",
       "      <td>-20.113596</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>586.697151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1962-02-01</td>\n",
       "      <td>608.536993</td>\n",
       "      <td>535.947809</td>\n",
       "      <td>556.833770</td>\n",
       "      <td>608.536993</td>\n",
       "      <td>608.536993</td>\n",
       "      <td>-62.537855</td>\n",
       "      <td>-62.537855</td>\n",
       "      <td>-62.537855</td>\n",
       "      <td>-62.537855</td>\n",
       "      <td>-62.537855</td>\n",
       "      <td>-62.537855</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>545.999137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1962-03-01</td>\n",
       "      <td>610.096182</td>\n",
       "      <td>631.409838</td>\n",
       "      <td>651.075875</td>\n",
       "      <td>610.096182</td>\n",
       "      <td>610.096182</td>\n",
       "      <td>31.410418</td>\n",
       "      <td>31.410418</td>\n",
       "      <td>31.410418</td>\n",
       "      <td>31.410418</td>\n",
       "      <td>31.410418</td>\n",
       "      <td>31.410418</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>641.506601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1962-04-01</td>\n",
       "      <td>611.822427</td>\n",
       "      <td>648.746992</td>\n",
       "      <td>668.799697</td>\n",
       "      <td>611.822427</td>\n",
       "      <td>611.822427</td>\n",
       "      <td>46.733066</td>\n",
       "      <td>46.733066</td>\n",
       "      <td>46.733066</td>\n",
       "      <td>46.733066</td>\n",
       "      <td>46.733066</td>\n",
       "      <td>46.733066</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>658.555494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1962-05-01</td>\n",
       "      <td>613.492987</td>\n",
       "      <td>710.633810</td>\n",
       "      <td>731.289465</td>\n",
       "      <td>613.492987</td>\n",
       "      <td>613.492987</td>\n",
       "      <td>108.064296</td>\n",
       "      <td>108.064296</td>\n",
       "      <td>108.064296</td>\n",
       "      <td>108.064296</td>\n",
       "      <td>108.064296</td>\n",
       "      <td>108.064296</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>721.557284</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ds       trend  yhat_lower  yhat_upper  trend_lower  trend_upper  \\\n",
       "0 1962-01-01  606.810747  577.139269  596.568861   606.810747   606.810747   \n",
       "1 1962-02-01  608.536993  535.947809  556.833770   608.536993   608.536993   \n",
       "2 1962-03-01  610.096182  631.409838  651.075875   610.096182   610.096182   \n",
       "3 1962-04-01  611.822427  648.746992  668.799697   611.822427   611.822427   \n",
       "4 1962-05-01  613.492987  710.633810  731.289465   613.492987   613.492987   \n",
       "\n",
       "   additive_terms  additive_terms_lower  additive_terms_upper      yearly  \\\n",
       "0      -20.113596            -20.113596            -20.113596  -20.113596   \n",
       "1      -62.537855            -62.537855            -62.537855  -62.537855   \n",
       "2       31.410418             31.410418             31.410418   31.410418   \n",
       "3       46.733066             46.733066             46.733066   46.733066   \n",
       "4      108.064296            108.064296            108.064296  108.064296   \n",
       "\n",
       "   yearly_lower  yearly_upper  multiplicative_terms  \\\n",
       "0    -20.113596    -20.113596                   0.0   \n",
       "1    -62.537855    -62.537855                   0.0   \n",
       "2     31.410418     31.410418                   0.0   \n",
       "3     46.733066     46.733066                   0.0   \n",
       "4    108.064296    108.064296                   0.0   \n",
       "\n",
       "   multiplicative_terms_lower  multiplicative_terms_upper        yhat  \n",
       "0                         0.0                         0.0  586.697151  \n",
       "1                         0.0                         0.0  545.999137  \n",
       "2                         0.0                         0.0  641.506601  \n",
       "3                         0.0                         0.0  658.555494  \n",
       "4                         0.0                         0.0  721.557284  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18.3808195"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(8.974783+27.786856)/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>yhat</th>\n",
       "      <th>yhat_lower</th>\n",
       "      <th>yhat_upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>1976-11-26</td>\n",
       "      <td>815.561801</td>\n",
       "      <td>804.126863</td>\n",
       "      <td>826.612997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>1976-11-27</td>\n",
       "      <td>818.770979</td>\n",
       "      <td>806.739664</td>\n",
       "      <td>829.425013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>1976-11-28</td>\n",
       "      <td>822.159021</td>\n",
       "      <td>811.360765</td>\n",
       "      <td>833.877451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>1976-11-29</td>\n",
       "      <td>825.663235</td>\n",
       "      <td>814.082143</td>\n",
       "      <td>837.356919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532</th>\n",
       "      <td>1976-11-30</td>\n",
       "      <td>829.213819</td>\n",
       "      <td>818.307056</td>\n",
       "      <td>840.706645</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ds        yhat  yhat_lower  yhat_upper\n",
       "528 1976-11-26  815.561801  804.126863  826.612997\n",
       "529 1976-11-27  818.770979  806.739664  829.425013\n",
       "530 1976-11-28  822.159021  811.360765  833.877451\n",
       "531 1976-11-29  825.663235  814.082143  837.356919\n",
       "532 1976-11-30  829.213819  818.307056  840.706645"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction[['ds','yhat','yhat_lower','yhat_upper']].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>yhat</th>\n",
       "      <th>yhat_lower</th>\n",
       "      <th>yhat_upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1962-01-01</td>\n",
       "      <td>586.697151</td>\n",
       "      <td>577.139269</td>\n",
       "      <td>596.568861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1962-02-01</td>\n",
       "      <td>545.999137</td>\n",
       "      <td>535.947809</td>\n",
       "      <td>556.833770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1962-03-01</td>\n",
       "      <td>641.506601</td>\n",
       "      <td>631.409838</td>\n",
       "      <td>651.075875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1962-04-01</td>\n",
       "      <td>658.555494</td>\n",
       "      <td>648.746992</td>\n",
       "      <td>668.799697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1962-05-01</td>\n",
       "      <td>721.557284</td>\n",
       "      <td>710.633810</td>\n",
       "      <td>731.289465</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ds        yhat  yhat_lower  yhat_upper\n",
       "0 1962-01-01  586.697151  577.139269  596.568861\n",
       "1 1962-02-01  545.999137  535.947809  556.833770\n",
       "2 1962-03-01  641.506601  631.409838  651.075875\n",
       "3 1962-04-01  658.555494  648.746992  668.799697\n",
       "4 1962-05-01  721.557284  710.633810  731.289465"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction[['ds','yhat','yhat_lower','yhat_upper']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting the Forecasts\n",
    " \n",
    "Prophet has an inbuilt feature that enables us to plot the forecasts we just generated. This is achieved using model.plot() and passing in our forecasts as the argument. The blue line in the graph represents the predicted values while the black dots represents the data in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### plot the predicted projection\n",
    "model.plot(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##### Visualize Each Components[Trends,Weekly]\n",
    "model.plot_components(prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cross Validation\n",
    " \n",
    "Next let’s measure the forecast error using the historical data. We’ll do this by comparing the predicted values with the actual values. In order to perform this operation we select cut of points in the history of the data and fit the model with data upto that cut off point. Afterwards we compare the actual values to the predicted values. The cross_validation method allows us to do this in Prophet. This method take the following parameters as explained below:\n",
    "\n",
    "1. horizon the forecast horizon\n",
    "2. initial the size of the initial training period\n",
    "3. period the spacing between cutoff dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(168, 2)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fbprophet.diagnostics import cross_validation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:fbprophet:Making 21 forecasts with cutoffs between 1965-01-22 00:00:00 and 1974-12-01 00:00:00\n"
     ]
    }
   ],
   "source": [
    "df_cv=cross_validation(model,horizon=\"365 days\",period='180 days',initial='1095 days')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1962-01-01</td>\n",
       "      <td>589.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1962-02-01</td>\n",
       "      <td>561.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1962-03-01</td>\n",
       "      <td>640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1962-04-01</td>\n",
       "      <td>656.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1962-05-01</td>\n",
       "      <td>727.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ds      y\n",
       "0 1962-01-01  589.0\n",
       "1 1962-02-01  561.0\n",
       "2 1962-03-01  640.0\n",
       "3 1962-04-01  656.0\n",
       "4 1962-05-01  727.0"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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>ds</th>\n",
       "      <th>yhat</th>\n",
       "      <th>yhat_lower</th>\n",
       "      <th>yhat_upper</th>\n",
       "      <th>y</th>\n",
       "      <th>cutoff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1965-02-01</td>\n",
       "      <td>629.706391</td>\n",
       "      <td>627.773764</td>\n",
       "      <td>631.361030</td>\n",
       "      <td>622.0</td>\n",
       "      <td>1965-01-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1965-03-01</td>\n",
       "      <td>712.739970</td>\n",
       "      <td>705.605921</td>\n",
       "      <td>718.567541</td>\n",
       "      <td>709.0</td>\n",
       "      <td>1965-01-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1965-04-01</td>\n",
       "      <td>731.118164</td>\n",
       "      <td>717.637752</td>\n",
       "      <td>743.232012</td>\n",
       "      <td>722.0</td>\n",
       "      <td>1965-01-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1965-05-01</td>\n",
       "      <td>800.091760</td>\n",
       "      <td>779.467091</td>\n",
       "      <td>820.481148</td>\n",
       "      <td>782.0</td>\n",
       "      <td>1965-01-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1965-06-01</td>\n",
       "      <td>768.349331</td>\n",
       "      <td>739.856680</td>\n",
       "      <td>798.558046</td>\n",
       "      <td>756.0</td>\n",
       "      <td>1965-01-22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ds        yhat  yhat_lower  yhat_upper      y     cutoff\n",
       "0 1965-02-01  629.706391  627.773764  631.361030  622.0 1965-01-22\n",
       "1 1965-03-01  712.739970  705.605921  718.567541  709.0 1965-01-22\n",
       "2 1965-04-01  731.118164  717.637752  743.232012  722.0 1965-01-22\n",
       "3 1965-05-01  800.091760  779.467091  820.481148  782.0 1965-01-22\n",
       "4 1965-06-01  768.349331  739.856680  798.558046  756.0 1965-01-22"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cv.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Obtaining the Performance Metrics\n",
    " \n",
    "We use the performance_metrics utility to compute the Mean Squared Error(MSE), Root Mean Squared Error(RMSE),Mean Absolute Error(MAE), Mean Absolute Percentage Error(MAPE) and the coverage of the the yhat_lower and yhat_upper estimates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>horizon</th>\n",
       "      <th>mse</th>\n",
       "      <th>rmse</th>\n",
       "      <th>mae</th>\n",
       "      <th>mape</th>\n",
       "      <th>mdape</th>\n",
       "      <th>coverage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>38 days</td>\n",
       "      <td>254.213828</td>\n",
       "      <td>15.944084</td>\n",
       "      <td>12.426328</td>\n",
       "      <td>0.015982</td>\n",
       "      <td>0.013692</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41 days</td>\n",
       "      <td>252.292064</td>\n",
       "      <td>15.883704</td>\n",
       "      <td>12.348837</td>\n",
       "      <td>0.015919</td>\n",
       "      <td>0.013692</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42 days</td>\n",
       "      <td>267.463848</td>\n",
       "      <td>16.354322</td>\n",
       "      <td>12.817196</td>\n",
       "      <td>0.016570</td>\n",
       "      <td>0.014528</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>43 days</td>\n",
       "      <td>268.236328</td>\n",
       "      <td>16.377922</td>\n",
       "      <td>12.952623</td>\n",
       "      <td>0.016783</td>\n",
       "      <td>0.014528</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46 days</td>\n",
       "      <td>253.593855</td>\n",
       "      <td>15.924630</td>\n",
       "      <td>12.602717</td>\n",
       "      <td>0.016295</td>\n",
       "      <td>0.014528</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  horizon         mse       rmse        mae      mape     mdape  coverage\n",
       "0 38 days  254.213828  15.944084  12.426328  0.015982  0.013692      0.36\n",
       "1 41 days  252.292064  15.883704  12.348837  0.015919  0.013692      0.36\n",
       "2 42 days  267.463848  16.354322  12.817196  0.016570  0.014528      0.36\n",
       "3 43 days  268.236328  16.377922  12.952623  0.016783  0.014528      0.36\n",
       "4 46 days  253.593855  15.924630  12.602717  0.016295  0.014528      0.36"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from fbprophet.diagnostics import performance_metrics\n",
    "df_performance=performance_metrics(df_cv)\n",
    "df_performance.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from fbprophet.plot import plot_cross_validation_metric\n",
    "fig=plot_cross_validation_metric(df_cv,metric='rmse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}