{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "block_hidden": true
   },
   "outputs": [],
   "source": [
    "%load_ext rpy2.ipython\n",
    "%matplotlib inline\n",
    "from fbprophet import Prophet\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import logging\n",
    "logging.getLogger('fbprophet').setLevel(logging.ERROR)\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "df = pd.read_csv('../examples/example_wp_log_peyton_manning.csv')\n",
    "m = Prophet()\n",
    "m.fit(df)\n",
    "future = m.make_future_dataframe(periods=366)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "block_hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Loading required package: Rcpp\n",
      "\n",
      "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Loading required package: rlang\n",
      "\n",
      "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "library(prophet)\n",
    "df <- read.csv('../examples/example_wp_log_peyton_manning.csv')\n",
    "m <- prophet(df)\n",
    "future <- make_future_dataframe(m, periods=366)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modeling Holidays and Special Events\n",
    "If you have holidays or other recurring events that you'd like to model, you must create a dataframe for them. It has two columns (`holiday` and `ds`) and a row for each occurrence of the holiday. It must include all occurrences of the holiday, both in the past (back as far as the historical data go) and in the future (out as far as the forecast is being made). If they won't repeat in the future, Prophet will model them and then not include them in the forecast.\n",
    "\n",
    "You can also include columns `lower_window` and `upper_window` which extend the holiday out to `[lower_window, upper_window]` days around the date. For instance, if you wanted to include Christmas Eve in addition to Christmas you'd include `lower_window=-1,upper_window=0`. If you wanted to use Black Friday in addition to Thanksgiving, you'd include `lower_window=0,upper_window=1`. You can also include a column `prior_scale` to set the prior scale separately for each holiday, as described below.\n",
    "\n",
    "Here we create a dataframe that includes the dates of all of Peyton Manning's playoff appearances:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "output_hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: \n",
      "Attaching package: ‘dplyr’\n",
      "\n",
      "\n",
      "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "\n",
      "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: The following objects are masked from ‘package:base’:\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "library(dplyr)\n",
    "playoffs <- data_frame(\n",
    "  holiday = 'playoff',\n",
    "  ds = as.Date(c('2008-01-13', '2009-01-03', '2010-01-16',\n",
    "                 '2010-01-24', '2010-02-07', '2011-01-08',\n",
    "                 '2013-01-12', '2014-01-12', '2014-01-19',\n",
    "                 '2014-02-02', '2015-01-11', '2016-01-17',\n",
    "                 '2016-01-24', '2016-02-07')),\n",
    "  lower_window = 0,\n",
    "  upper_window = 1\n",
    ")\n",
    "superbowls <- data_frame(\n",
    "  holiday = 'superbowl',\n",
    "  ds = as.Date(c('2010-02-07', '2014-02-02', '2016-02-07')),\n",
    "  lower_window = 0,\n",
    "  upper_window = 1\n",
    ")\n",
    "holidays <- bind_rows(playoffs, superbowls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "playoffs = pd.DataFrame({\n",
    "  'holiday': 'playoff',\n",
    "  'ds': pd.to_datetime(['2008-01-13', '2009-01-03', '2010-01-16',\n",
    "                        '2010-01-24', '2010-02-07', '2011-01-08',\n",
    "                        '2013-01-12', '2014-01-12', '2014-01-19',\n",
    "                        '2014-02-02', '2015-01-11', '2016-01-17',\n",
    "                        '2016-01-24', '2016-02-07']),\n",
    "  'lower_window': 0,\n",
    "  'upper_window': 1,\n",
    "})\n",
    "superbowls = pd.DataFrame({\n",
    "  'holiday': 'superbowl',\n",
    "  'ds': pd.to_datetime(['2010-02-07', '2014-02-02', '2016-02-07']),\n",
    "  'lower_window': 0,\n",
    "  'upper_window': 1,\n",
    "})\n",
    "holidays = pd.concat((playoffs, superbowls))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above we have included the superbowl days as both playoff games and superbowl games. This means that the superbowl effect will be an additional additive bonus on top of the playoff effect.\n",
    "\n",
    "Once the table is created, holiday effects are included in the forecast by passing them in with the `holidays` argument. Here we do it with the Peyton Manning data from the [Quickstart](https://facebook.github.io/prophet/docs/quick_start.html):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "output_hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "m <- prophet(df, holidays = holidays)\n",
    "forecast <- predict(m, future)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = Prophet(holidays=holidays)\n",
    "forecast = m.fit(df).predict(future)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The holiday effect can be seen in the `forecast` dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "output_hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "           ds  playoff superbowl\n",
       "17 2014-02-02 1.219683  1.201974\n",
       "18 2014-02-03 1.898971  1.460981\n",
       "19 2015-01-11 1.219683  0.000000\n",
       "20 2015-01-12 1.898971  0.000000\n",
       "21 2016-01-17 1.219683  0.000000\n",
       "22 2016-01-18 1.898971  0.000000\n",
       "23 2016-01-24 1.219683  0.000000\n",
       "24 2016-01-25 1.898971  0.000000\n",
       "25 2016-02-07 1.219683  1.201974\n",
       "26 2016-02-08 1.898971  1.460981\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "forecast %>% \n",
    "  select(ds, playoff, superbowl) %>% \n",
    "  filter(abs(playoff + superbowl) > 0) %>%\n",
    "  tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>playoff</th>\n",
       "      <th>superbowl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2190</th>\n",
       "      <td>2014-02-02</td>\n",
       "      <td>1.217571</td>\n",
       "      <td>1.230312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2191</th>\n",
       "      <td>2014-02-03</td>\n",
       "      <td>1.898042</td>\n",
       "      <td>1.466063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2532</th>\n",
       "      <td>2015-01-11</td>\n",
       "      <td>1.217571</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2533</th>\n",
       "      <td>2015-01-12</td>\n",
       "      <td>1.898042</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2901</th>\n",
       "      <td>2016-01-17</td>\n",
       "      <td>1.217571</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2902</th>\n",
       "      <td>2016-01-18</td>\n",
       "      <td>1.898042</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2908</th>\n",
       "      <td>2016-01-24</td>\n",
       "      <td>1.217571</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2909</th>\n",
       "      <td>2016-01-25</td>\n",
       "      <td>1.898042</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2922</th>\n",
       "      <td>2016-02-07</td>\n",
       "      <td>1.217571</td>\n",
       "      <td>1.230312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2923</th>\n",
       "      <td>2016-02-08</td>\n",
       "      <td>1.898042</td>\n",
       "      <td>1.466063</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ds   playoff  superbowl\n",
       "2190 2014-02-02  1.217571   1.230312\n",
       "2191 2014-02-03  1.898042   1.466063\n",
       "2532 2015-01-11  1.217571   0.000000\n",
       "2533 2015-01-12  1.898042   0.000000\n",
       "2901 2016-01-17  1.217571   0.000000\n",
       "2902 2016-01-18  1.898042   0.000000\n",
       "2908 2016-01-24  1.217571   0.000000\n",
       "2909 2016-01-25  1.898042   0.000000\n",
       "2922 2016-02-07  1.217571   1.230312\n",
       "2923 2016-02-08  1.898042   1.466063"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast[(forecast['playoff'] + forecast['superbowl']).abs() > 0][\n",
    "        ['ds', 'playoff', 'superbowl']][-10:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The holiday effects will also show up in the components plot, where we see that there is a spike on the days around playoff appearances, with an especially large spike for the superbowl:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "output_hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "[encoded data, 68724 characters]\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 9 -h 12 -u in\n",
    "prophet_plot_components(m, forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "[encoded data, 83484 characters]\n",
      "text/plain": [
       "<Figure size 648x864 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = m.plot_components(forecast)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Individual holidays can be plotted using the `plot_forecast_component` function (imported from `fbprophet.plot` in Python) like `plot_forecast_component(m, forecast, 'superbowl')` to plot just the superbowl holiday component."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Built-in Country Holidays\n",
    "\n",
    "You can use a built-in collection of country-specific holidays using the `add_country_holidays` method (Python) or function (R). The name of the country is specified, and then major holidays for that country will be included in addition to any holidays that are specified via the `holidays` argument described above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "output_hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "m <- prophet(holidays = holidays)\n",
    "m <- add_country_holidays(m, country_name = 'US')\n",
    "m <- fit.prophet(m, df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "output_hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<fbprophet.forecaster.Prophet at 0x7fa913d94780>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = Prophet(holidays=holidays)\n",
    "m.add_country_holidays(country_name='US')\n",
    "m.fit(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see which holidays were included by looking at the `train_holiday_names` (Python) or `train.holiday.names` (R) attribute of the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " [1] \"playoff\"                     \"superbowl\"                  \n",
       " [3] \"New Year's Day\"              \"Martin Luther King, Jr. Day\"\n",
       " [5] \"Washington's Birthday\"       \"Memorial Day\"               \n",
       " [7] \"Independence Day\"            \"Labor Day\"                  \n",
       " [9] \"Columbus Day\"                \"Veterans Day\"               \n",
       "[11] \"Veterans Day (Observed)\"     \"Thanksgiving\"               \n",
       "[13] \"Christmas Day\"               \"Independence Day (Observed)\"\n",
       "[15] \"Christmas Day (Observed)\"    \"New Year's Day (Observed)\"  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "m$train.holiday.names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                         playoff\n",
       "1                       superbowl\n",
       "2                  New Year's Day\n",
       "3     Martin Luther King, Jr. Day\n",
       "4           Washington's Birthday\n",
       "5                    Memorial Day\n",
       "6                Independence Day\n",
       "7                       Labor Day\n",
       "8                    Columbus Day\n",
       "9                    Veterans Day\n",
       "10                   Thanksgiving\n",
       "11                  Christmas Day\n",
       "12       Christmas Day (Observed)\n",
       "13        Veterans Day (Observed)\n",
       "14    Independence Day (Observed)\n",
       "15      New Year's Day (Observed)\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.train_holiday_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The holidays for each country are provided by the `holidays` package in Python. A list of available countries, and the country name to use, is available on their page: https://github.com/dr-prodigy/python-holidays. In addition to those countries, Prophet includes holidays for these countries: Brazil (BR), Indonesia (ID), India (IN), Malaysia (MY), Vietnam (VN), Thailand (TH), Philippines (PH), Turkey (TU), Pakistan (PK), Bangladesh (BD), Egypt (EG), China (CN), and Russian (RU).\n",
    "\n",
    "In Python, most holidays are computed deterministically and so are available for any date range; a warning will be raised if dates fall outside the range supported by that country. In R, holiday dates are computed for 1995 through 2044 and stored in the package as `data-raw/generated_holidays.csv`. If a wider date range is needed, this script can be used to replace that file with a different date range: https://github.com/facebook/prophet/blob/master/python/scripts/generate_holidays_file.py.\n",
    "\n",
    "As above, the country-level holidays will then show up in the components plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "output_hidden": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "[encoded data, 76676 characters]\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 9 -h 12 -u in\n",
    "forecast <- predict(m, future)\n",
    "prophet_plot_components(m, forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "[encoded data, 86668 characters]\n",
      "text/plain": [
       "<Figure size 648x864 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "forecast = m.predict(future)\n",
    "fig = m.plot_components(forecast)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fourier Order for Seasonalities\n",
    "\n",
    "Seasonalities are estimated using a partial Fourier sum. See [the paper](https://peerj.com/preprints/3190/) for complete details, and [this figure on Wikipedia](https://en.wikipedia.org/wiki/Fourier_series#/media/File:Fourier_Series.svg) for an illustration of how a partial Fourier sum can approximate an aribtrary periodic signal. The number of terms in the partial sum (the order) is a parameter that determines how quickly the seasonality can change. To illustrate this, consider the Peyton Manning data from the [Quickstart](https://facebook.github.io/prophet/docs/quick_start.html). The default Fourier order for yearly seasonality is 10, which produces this fit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "output_hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "[encoded data, 22084 characters]\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 9 -h 3 -u in\n",
    "m <- prophet(df)\n",
    "prophet:::plot_yearly(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "[encoded data, 38128 characters]\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from fbprophet.plot import plot_yearly\n",
    "m = Prophet().fit(df)\n",
    "a = plot_yearly(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The default values are often appropriate, but they can be increased when the seasonality needs to fit higher-frequency changes, and generally be less smooth. The Fourier order can be specified for each built-in seasonality when instantiating the model, here it is increased to 20:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "output_hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "[encoded data, 22836 characters]\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 9 -h 3 -u in\n",
    "m <- prophet(df, yearly.seasonality = 20)\n",
    "prophet:::plot_yearly(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "[encoded data, 35748 characters]\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from fbprophet.plot import plot_yearly\n",
    "m = Prophet(yearly_seasonality=20).fit(df)\n",
    "a = plot_yearly(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Increasing the number of Fourier terms allows the seasonality to fit faster changing cycles, but can also lead to overfitting: N Fourier terms corresponds to 2N variables used for modeling the cycle\n",
    "\n",
    "### Specifying Custom Seasonalities\n",
    "\n",
    "Prophet will by default fit weekly and yearly seasonalities, if the time series is more than two cycles long. It will also fit daily seasonality for a sub-daily time series. You can add other seasonalities (monthly, quarterly, hourly) using the `add_seasonality` method (Python) or function (R).\n",
    "\n",
    "The inputs to this function are a name, the period of the seasonality in days, and the Fourier order for the seasonality. For reference, by default Prophet uses a Fourier order of 3 for weekly seasonality and 10 for yearly seasonality. An optional input to `add_seasonality` is the prior scale for that seasonal component - this is discussed below.\n",
    "\n",
    "As an example, here we fit the Peyton Manning data from the [Quickstart](https://facebook.github.io/prophet/docs/quick_start.html), but replace the weekly seasonality with monthly seasonality. The monthly seasonality then will appear in the components plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "output_hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "[encoded data, 59320 characters]\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 9 -h 9 -u in\n",
    "m <- prophet(weekly.seasonality=FALSE)\n",
    "m <- add_seasonality(m, name='monthly', period=30.5, fourier.order=5)\n",
    "m <- fit.prophet(m, df)\n",
    "forecast <- predict(m, future)\n",
    "prophet_plot_components(m, forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "[encoded data, 72764 characters]\n",
      "text/plain": [
       "<Figure size 648x648 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m = Prophet(weekly_seasonality=False)\n",
    "m.add_seasonality(name='monthly', period=30.5, fourier_order=5)\n",
    "forecast = m.fit(df).predict(future)\n",
    "fig = m.plot_components(forecast)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seasonalities that depend on other factors\n",
    "In some instances the seasonality may depend on other factors, such as a weekly seasonal pattern that is different during the summer than it is during the rest of the year, or a daily seasonal pattern that is different on weekends vs. on weekdays. These types of seasonalities can be modeled using conditional seasonalities.\n",
    "\n",
    "Consider the Peyton Manning example from the [Quickstart](https://facebook.github.io/prophet/docs/quick_start.html). The default weekly seasonality assumes that the pattern of weekly seasonality is the same throughout the year, but we'd expect the pattern of weekly seasonality to be different during the on-season (when there are games every Sunday) and the off-season. We can use conditional seasonalities to construct separate on-season and off-season weekly seasonalities.\n",
    "\n",
    "First we add a boolean column to the dataframe that indicates whether each date is during the on-season or the off-season:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "is_nfl_season <- function(ds) {\n",
    "  dates <- as.Date(ds)\n",
    "  month <- as.numeric(format(dates, '%m'))\n",
    "  return(month > 8 | month < 2)\n",
    "}\n",
    "df$on_season <- is_nfl_season(df$ds)\n",
    "df$off_season <- !is_nfl_season(df$ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_nfl_season(ds):\n",
    "    date = pd.to_datetime(ds)\n",
    "    return (date.month > 8 or date.month < 2)\n",
    "\n",
    "df['on_season'] = df['ds'].apply(is_nfl_season)\n",
    "df['off_season'] = ~df['ds'].apply(is_nfl_season)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we disable the built-in weekly seasonality, and replace it with two weekly seasonalities that have these columns specified as a condition. This means that the seasonality will only be applied to dates where the `condition_name` column is `True`. We must also add the column to the `future` dataframe for which we are making predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "output_hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:rpy2.rinterface_lib.callbacks:R[write to console]: Disabling daily seasonality. Run prophet with daily.seasonality=TRUE to override this.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "[encoded data, 76844 characters]\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 9 -h 12 -u in\n",
    "m <- prophet(weekly.seasonality=FALSE)\n",
    "m <- add_seasonality(m, name='weekly_on_season', period=7, fourier.order=3, condition.name='on_season')\n",
    "m <- add_seasonality(m, name='weekly_off_season', period=7, fourier.order=3, condition.name='off_season')\n",
    "m <- fit.prophet(m, df)\n",
    "\n",
    "future$on_season <- is_nfl_season(future$ds)\n",
    "future$off_season <- !is_nfl_season(future$ds)\n",
    "forecast <- predict(m, future)\n",
    "prophet_plot_components(m, forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "[encoded data, 95868 characters]\n",
      "text/plain": [
       "<Figure size 648x864 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m = Prophet(weekly_seasonality=False)\n",
    "m.add_seasonality(name='weekly_on_season', period=7, fourier_order=3, condition_name='on_season')\n",
    "m.add_seasonality(name='weekly_off_season', period=7, fourier_order=3, condition_name='off_season')\n",
    "\n",
    "future['on_season'] = future['ds'].apply(is_nfl_season)\n",
    "future['off_season'] = ~future['ds'].apply(is_nfl_season)\n",
    "forecast = m.fit(df).predict(future)\n",
    "fig = m.plot_components(forecast)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both of the seasonalities now show up in the components plots above. We can see that during the on-season when games are played every Sunday, there are large increases on Sunday and Monday that are completely absent during the off-season."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prior scale for holidays and seasonality\n",
    "If you find that the holidays are overfitting, you can adjust their prior scale to smooth them using the parameter `holidays_prior_scale`. By default this parameter is 10, which provides very little regularization. Reducing this parameter dampens holiday effects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "output_hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Initial log joint probability = -19.4685\n",
       "Optimization terminated normally: \n",
       "  Convergence detected: relative gradient magnitude is below tolerance\n",
       "           ds  playoff superbowl\n",
       "17 2014-02-02 1.216714 0.9343464\n",
       "18 2014-02-03 1.858898 0.9939908\n",
       "19 2015-01-11 1.216714 0.0000000\n",
       "20 2015-01-12 1.858898 0.0000000\n",
       "21 2016-01-17 1.216714 0.0000000\n",
       "22 2016-01-18 1.858898 0.0000000\n",
       "23 2016-01-24 1.216714 0.0000000\n",
       "24 2016-01-25 1.858898 0.0000000\n",
       "25 2016-02-07 1.216714 0.9343464\n",
       "26 2016-02-08 1.858898 0.9939908\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "m <- prophet(df, holidays = holidays, holidays.prior.scale = 0.05)\n",
    "forecast <- predict(m, future)\n",
    "forecast %>% \n",
    "  select(ds, playoff, superbowl) %>% \n",
    "  filter(abs(playoff + superbowl) > 0) %>%\n",
    "  tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>playoff</th>\n",
       "      <th>superbowl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2190</th>\n",
       "      <td>2014-02-02</td>\n",
       "      <td>1.205344</td>\n",
       "      <td>0.963327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2191</th>\n",
       "      <td>2014-02-03</td>\n",
       "      <td>1.851992</td>\n",
       "      <td>0.991010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2532</th>\n",
       "      <td>2015-01-11</td>\n",
       "      <td>1.205344</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2533</th>\n",
       "      <td>2015-01-12</td>\n",
       "      <td>1.851992</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2901</th>\n",
       "      <td>2016-01-17</td>\n",
       "      <td>1.205344</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2902</th>\n",
       "      <td>2016-01-18</td>\n",
       "      <td>1.851992</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2908</th>\n",
       "      <td>2016-01-24</td>\n",
       "      <td>1.205344</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2909</th>\n",
       "      <td>2016-01-25</td>\n",
       "      <td>1.851992</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2922</th>\n",
       "      <td>2016-02-07</td>\n",
       "      <td>1.205344</td>\n",
       "      <td>0.963327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2923</th>\n",
       "      <td>2016-02-08</td>\n",
       "      <td>1.851992</td>\n",
       "      <td>0.991010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             ds   playoff  superbowl\n",
       "2190 2014-02-02  1.205344   0.963327\n",
       "2191 2014-02-03  1.851992   0.991010\n",
       "2532 2015-01-11  1.205344   0.000000\n",
       "2533 2015-01-12  1.851992   0.000000\n",
       "2901 2016-01-17  1.205344   0.000000\n",
       "2902 2016-01-18  1.851992   0.000000\n",
       "2908 2016-01-24  1.205344   0.000000\n",
       "2909 2016-01-25  1.851992   0.000000\n",
       "2922 2016-02-07  1.205344   0.963327\n",
       "2923 2016-02-08  1.851992   0.991010"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = Prophet(holidays=holidays, holidays_prior_scale=0.05).fit(df)\n",
    "forecast = m.predict(future)\n",
    "forecast[(forecast['playoff'] + forecast['superbowl']).abs() > 0][\n",
    "    ['ds', 'playoff', 'superbowl']][-10:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The magnitude of the holiday effect has been reduced compared to before, especially for superbowls, which had the fewest observations. There is a parameter `seasonality_prior_scale` which similarly adjusts the extent to which the seasonality model will fit the data.\n",
    "\n",
    "Prior scales can be set separately for individual holidays by including a column `prior_scale` in the holidays dataframe. Prior scales for individual seasonalities can be passed as an argument to `add_seasonality`. For instance, the prior scale for just weekly seasonality can be set using:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%R\n",
    "m <- prophet()\n",
    "m <- add_seasonality(\n",
    "  m, name='weekly', period=7, fourier.order=3, prior.scale=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "output_hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<fbprophet.forecaster.Prophet at 0x7f1f41e1b090>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = Prophet()\n",
    "m.add_seasonality(\n",
    "    name='weekly', period=7, fourier_order=3, prior_scale=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Additional regressors\n",
    "Additional regressors can be added to the linear part of the model using the `add_regressor` method or function. A column with the regressor value will need to be present in both the fitting and prediction dataframes. For example, we can add an additional effect on Sundays during the NFL season. On the components plot, this effect will show up in the 'extra_regressors' plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "output_hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Initial log joint probability = -19.4685\n",
       "Optimization terminated normally: \n",
       "  Convergence detected: relative gradient magnitude is below tolerance\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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flU0QxMmTJ0+fPi0Wi/v169ehQwerHKy62lbPN5fLpShKp2vuLHSXsyumbL/e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\nQUwEFswIgiAIYiKwYEYQBEEQE/H/AYI8bFO1rApkAAAAAElFTkSuQmCC\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 9 -h 12 -u in\n",
    "nfl_sunday <- function(ds) {\n",
    "  dates <- as.Date(ds)\n",
    "  month <- as.numeric(format(dates, '%m'))\n",
    "  as.numeric((weekdays(dates) == \"Sunday\") & (month > 8 | month < 2))\n",
    "}\n",
    "df$nfl_sunday <- nfl_sunday(df$ds)\n",
    "\n",
    "m <- prophet()\n",
    "m <- add_regressor(m, 'nfl_sunday')\n",
    "m <- fit.prophet(m, df)\n",
    "\n",
    "future$nfl_sunday <- nfl_sunday(future$ds)\n",
    "\n",
    "forecast <- predict(m, future)\n",
    "prophet_plot_components(m, forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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in3msREXapZUAWFxcjEWLFiEnJwcq1f9PavnDDz9oY3iT0ifCA3+90g3Dlqbj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\n",
      "text/plain": [
       "<Figure size 648x864 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def nfl_sunday(ds):\n",
    "    date = pd.to_datetime(ds)\n",
    "    if date.weekday() == 6 and (date.month > 8 or date.month < 2):\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "df['nfl_sunday'] = df['ds'].apply(nfl_sunday)\n",
    "\n",
    "m = Prophet()\n",
    "m.add_regressor('nfl_sunday')\n",
    "m.fit(df)\n",
    "\n",
    "future['nfl_sunday'] = future['ds'].apply(nfl_sunday)\n",
    "\n",
    "forecast = m.predict(future)\n",
    "fig = m.plot_components(forecast)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NFL Sundays could also have been handled using the \"holidays\" interface described above, by creating a list of past and future NFL Sundays. The `add_regressor` function provides a more general interface for defining extra linear regressors, and in particular does not require that the regressor be a binary indicator. Another time series could be used as a regressor, although its future values would have to be known.\n",
    "\n",
    "[This notebook](https://nbviewer.jupyter.org/github/nicolasfauchereau/Auckland_Cycling/blob/master/notebooks/Auckland_cycling_and_weather.ipynb) shows an example of using weather factors as extra regressors in a forecast of bicycle usage, and provides an excellent illustration of how other time series can be included as extra regressors.\n",
    "\n",
    "The `add_regressor` function has optional arguments for specifying the prior scale (holiday prior scale is used by default) and whether or not the regressor is standardized - see the docstring with `help(Prophet.add_regressor)` in Python and `?add_regressor` in R. Note that regressors must be added prior to model fitting.\n",
    "\n",
    "The extra regressor must be known for both the history and for future dates. It thus must either be something that has known future values (such as `nfl_sunday`), or something that has separately been forecasted elsewhere. Prophet will also raise an error if the regressor is constant throughout the history, since there is nothing to fit from it.\n",
    "\n",
    "Extra regressors are put in the linear component of the model, so the underlying model is that the time series depends on the extra regressor as either an additive or multiplicative factor (see the next section for multiplicativity)."
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Edit 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,
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}
