22_Prophet_project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "42d4ee66",
   "metadata": {},
   "source": [
    "# Forecasting with Prophet "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d9922f94",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "from itertools import product\n",
    "from fbprophet import Prophet\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from fbprophet.diagnostics import cross_validation\n",
    "from fbprophet.diagnostics import performance_metrics\n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5d591089",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (9,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9d41492f",
   "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>co2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1958-03-29</th>\n",
       "      <td>316.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-05</th>\n",
       "      <td>317.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-12</th>\n",
       "      <td>317.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-19</th>\n",
       "      <td>317.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-26</th>\n",
       "      <td>316.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              co2\n",
       "1958-03-29  316.1\n",
       "1958-04-05  317.3\n",
       "1958-04-12  317.6\n",
       "1958-04-19  317.5\n",
       "1958-04-26  316.4"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = sm.datasets.co2.load_pandas().data\n",
    "df = df.interpolate()\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8e81f1db",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.plot(df['co2'])\n",
    "ax.set_xlabel('Time')\n",
    "ax.set_ylabel('CO2 concentration (ppmw)')\n",
    "\n",
    "fig.autofmt_xdate()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6953bd9",
   "metadata": {},
   "source": [
    "## Format data for Prophet "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "68549cb2",
   "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>index</th>\n",
       "      <th>co2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1958-03-29</td>\n",
       "      <td>316.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1958-04-05</td>\n",
       "      <td>317.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1958-04-12</td>\n",
       "      <td>317.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1958-04-19</td>\n",
       "      <td>317.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1958-04-26</td>\n",
       "      <td>316.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       index    co2\n",
       "0 1958-03-29  316.1\n",
       "1 1958-04-05  317.3\n",
       "2 1958-04-12  317.6\n",
       "3 1958-04-19  317.5\n",
       "4 1958-04-26  316.4"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.reset_index()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "aa148001",
   "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>1958-03-29</td>\n",
       "      <td>316.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1958-04-05</td>\n",
       "      <td>317.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1958-04-12</td>\n",
       "      <td>317.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1958-04-19</td>\n",
       "      <td>317.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1958-04-26</td>\n",
       "      <td>316.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ds      y\n",
       "0 1958-03-29  316.1\n",
       "1 1958-04-05  317.3\n",
       "2 1958-04-12  317.6\n",
       "3 1958-04-19  317.5\n",
       "4 1958-04-26  316.4"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = ['ds', 'y']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fe0ddba",
   "metadata": {},
   "source": [
    "## Hyperparameter tuning "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "70d11e1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = df[:-104]\n",
    "test = df[-104:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3e0d0544",
   "metadata": {
    "collapsed": true
   },
   "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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ff8f587e8fa94283bc4bf6e4664f3846",
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    {
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     "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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "655b830b9d104c9586b1a30451ac6bb3",
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    },
    {
     "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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b4572d940e564b9c8977cb265d87ba89",
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    },
    {
     "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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "312f6b2bfb0f4c18ac9c10d0ac0ca493",
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    },
    {
     "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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "755938c36980430f9772d1b9d9d0bd61",
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    },
    {
     "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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e77463941d2549a5b2e8ba95562b1280",
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    },
    {
     "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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a974676bbee44bc8ba77715e490ed7ae",
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    },
    {
     "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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5db63904ad494cc0be1b7c726ac11783",
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    },
    {
     "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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d0389dbec7b1487288ba23072913ee50",
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    },
    {
     "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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "96f6d1857fcd430ea679f83d61f5f951",
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     "metadata": {},
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    },
    {
     "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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b09b96325cc34c55a8239adf6eaf604b",
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "09c2afd7779f4810b2e05e8bb1d1f079",
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     "metadata": {},
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    },
    {
     "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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b6333b509a4f41f7b34e596f0823fc26",
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     "metadata": {},
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    },
    {
     "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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f56732c9783347e8900d4be668111df4",
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     "metadata": {},
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    },
    {
     "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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b5a3884d18f546cfbecba744b4a18f85",
       "version_major": 2,
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      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ff6b4807d95b44438d5e47cd47c3cd24",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "param_grid = {\n",
    "    'changepoint_prior_scale': [0.001, 0.01, 0.1, 0.5],\n",
    "    'seasonality_prior_scale': [0.01, 0.1, 1.0, 10.0]\n",
    "}\n",
    "\n",
    "params = [dict(zip(param_grid.keys(), v)) for v in product(*param_grid.values())]\n",
    "\n",
    "mses = []\n",
    "\n",
    "cutoffs = pd.date_range(start='1990-03-29', end='2000-01-01', freq='52W')\n",
    "\n",
    "for param in params:\n",
    "    m = Prophet(**param)\n",
    "    m.fit(train)\n",
    "    \n",
    "    df_cv = cross_validation(model=m, horizon='365 days', cutoffs=cutoffs)\n",
    "    df_p = performance_metrics(df_cv, rolling_window=1)\n",
    "    mses.append(df_p['mse'].values[0])\n",
    "    \n",
    "tuning_results = pd.DataFrame(params)\n",
    "tuning_results['mse'] = mses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d4b1d3a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'changepoint_prior_scale': 0.001, 'seasonality_prior_scale': 0.01}\n"
     ]
    }
   ],
   "source": [
    "best_params = params[np.argmin(mses)]\n",
    "\n",
    "print(best_params)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4777f9cf",
   "metadata": {},
   "source": [
    "## Rolling forecast "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "2f466c7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rolling_forecast(df: pd.DataFrame, train_len: int, horizon: int, window: int, method: str) -> list:\n",
    "    \n",
    "    total_len = train_len + horizon\n",
    "\n",
    "    if method == 'prophet':\n",
    "        pred_prophet = []\n",
    "        \n",
    "        for i in range(train_len, total_len, window):\n",
    "            m = Prophet(**best_params)\n",
    "            m.fit(df[:i])\n",
    "            future = m.make_future_dataframe(periods=52, freq='W')\n",
    "            forecast = m.predict(future)\n",
    "            pred_prophet.extend(forecast[-52:]['yhat'].values)\n",
    "            \n",
    "        return pred_prophet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6909877d",
   "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",
      "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/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",
       "      <th>pred_prophet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2180</th>\n",
       "      <td>2000-01-08</td>\n",
       "      <td>368.5</td>\n",
       "      <td>367.895561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2181</th>\n",
       "      <td>2000-01-15</td>\n",
       "      <td>369.0</td>\n",
       "      <td>368.121547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2182</th>\n",
       "      <td>2000-01-22</td>\n",
       "      <td>369.8</td>\n",
       "      <td>368.302782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2183</th>\n",
       "      <td>2000-01-29</td>\n",
       "      <td>369.2</td>\n",
       "      <td>368.464940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2184</th>\n",
       "      <td>2000-02-05</td>\n",
       "      <td>369.1</td>\n",
       "      <td>368.641222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ds      y  pred_prophet\n",
       "2180 2000-01-08  368.5    367.895561\n",
       "2181 2000-01-15  369.0    368.121547\n",
       "2182 2000-01-22  369.8    368.302782\n",
       "2183 2000-01-29  369.2    368.464940\n",
       "2184 2000-02-05  369.1    368.641222"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TRAIN_LEN = len(train)\n",
    "HORIZON = len(test)\n",
    "WINDOW = 52\n",
    "\n",
    "pred_prophet = rolling_forecast(df, TRAIN_LEN, HORIZON, WINDOW, 'prophet')\n",
    "\n",
    "test = test.copy()\n",
    "\n",
    "test.loc[:, 'pred_prophet'] = pred_prophet\n",
    "\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8240775b",
   "metadata": {},
   "source": [
    "## Evaluation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "12111d6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mape(y_true, y_pred):\n",
    "    return round(np.mean(np.abs((y_true - y_pred) / y_true)) * 100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "8c1bd831",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.13\n"
     ]
    }
   ],
   "source": [
    "mape_prophet = mape(test['y'], test['pred_prophet'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2bac21c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "x = ['Baseline', 'Theta', 'TES', 'SARIMA', 'Prophet']\n",
    "y = [0.36, 0.28, 0.12, 0.09, mape_prophet]\n",
    "\n",
    "ax.bar(x, y, width=0.4)\n",
    "ax.set_xlabel('Exponential smoothing models')\n",
    "ax.set_ylabel('MAPE (%)')\n",
    "ax.set_ylim(0, 1)\n",
    "\n",
    "for index, value in enumerate(y):\n",
    "    plt.text(x=index, y=value + 0.05, s=str(value), ha='center')\n",
    "    \n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90b61609",
   "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": "3.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}