fbprophet/fbprophet_holidays.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook covers the use of 'holidays' in the Prophet forecasting library. In this notebook, we will extend the previous example (http://pythondata.com/forecasting-time-series-data-prophet-jupyter-notebook/) to use holidays in the forecasting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from fbprophet import Prophet\n",
    "import matplotlib.pyplot as plt\n",
    " \n",
    "%matplotlib inline\n",
    " \n",
    "plt.rcParams['figure.figsize']=(20,10)\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read in the data\n",
    "\n",
    "Read the data in from the retail sales CSV file in the examples folder then set the index to the 'date' column. We are also parsing dates in the data file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sales_df = pd.read_csv('../examples/retail_sales.csv', index_col='date', parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-10-01</th>\n",
       "      <td>338630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-11-01</th>\n",
       "      <td>339386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12-01</th>\n",
       "      <td>400264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>314640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-01</th>\n",
       "      <td>311022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             sales\n",
       "date              \n",
       "2009-10-01  338630\n",
       "2009-11-01  339386\n",
       "2009-12-01  400264\n",
       "2010-01-01  314640\n",
       "2010-02-01  311022"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare for Prophet\n",
    "\n",
    "As explained in previous prophet posts, for prophet to work, we need to change the names of these columns to 'ds' and 'y'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = sales_df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-10-01</td>\n",
       "      <td>338630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-11-01</td>\n",
       "      <td>339386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>400264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>314640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-02-01</td>\n",
       "      <td>311022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date   sales\n",
       "0 2009-10-01  338630\n",
       "1 2009-11-01  339386\n",
       "2 2009-12-01  400264\n",
       "3 2010-01-01  314640\n",
       "4 2010-02-01  311022"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's rename the columns as required by fbprophet. Additioinally, fbprophet doesn't like the index to be a datetime...it wants to see 'ds' as a non-index column, so we won't set an index differnetly than the integer index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df=df.rename(columns={'date':'ds', 'sales':'y'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>2009-10-01</td>\n",
       "      <td>338630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009-11-01</td>\n",
       "      <td>339386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>400264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>314640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-02-01</td>\n",
       "      <td>311022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ds       y\n",
       "0 2009-10-01  338630\n",
       "1 2009-11-01  339386\n",
       "2 2009-12-01  400264\n",
       "3 2010-01-01  314640\n",
       "4 2010-02-01  311022"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now's a good time to take a look at your data.  Plot the data using pandas' ```plot``` function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa42a82ef90>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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X0WTzPGjSsUPS4EDS1UTGGJnrb5ZOvSG17knppe2P/106fli+37xdJvZvsUdM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\n9ZyV76P3yFx6pffn98ocGgZuBwfc71+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sYeBZoUkDwZVSI9GDKaI7FbFMyZ2WzwGZjnPL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\nELAd+0cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa458725850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.set_index('ds').y.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reviewing the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see from this data that there is a spike in the same month each year. While spike could be due to many different reasons, let's assume its because there's a major promotion that this company runs every year at that time, which is in December for this dataset.\n",
    "\n",
    "Because we know this promotion occurs every december, we want to use this knowledge to help prophet better forecast those months, so we'll use prohpet's ```holiday``` construct (explained here https://facebookincubator.github.io/prophet/docs/holiday_effects.html).\n",
    "\n",
    "The holiday constrict is a pandas dataframe with the holiday and date of the holiday. For this example, the construct would look like this:\n",
    "\n",
    "```promotions = pd.DataFrame({\n",
    "  'holiday': 'december_promotion',\n",
    "  'ds': pd.to_datetime(['2009-12-01', '2010-12-01', '2011-12-01', '2012-12-01',\n",
    "                        '2013-12-01', '2014-12-01', '2015-12-01']),\n",
    "  'lower_window': 0,\n",
    "  'upper_window': 0,\n",
    "})```\n",
    "\n",
    "This ```promotions``` dataframe consisists of promotion dates for Dec in 2009 through 2015,  The ```lower_window``` and ```upper_window``` values are set to zero to indicate that we don't want prophet to consider any other months than the ones listed.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "promotions = pd.DataFrame({\n",
    "  'holiday': 'december_promotion',\n",
    "  'ds': pd.to_datetime(['2009-12-01', '2010-12-01', '2011-12-01', '2012-12-01',\n",
    "                        '2013-12-01', '2014-12-01', '2015-12-01']),\n",
    "  'lower_window': 0,\n",
    "  'upper_window': 0,\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>holiday</th>\n",
       "      <th>lower_window</th>\n",
       "      <th>upper_window</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-12-01</td>\n",
       "      <td>december_promotion</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>december_promotion</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-12-01</td>\n",
       "      <td>december_promotion</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-12-01</td>\n",
       "      <td>december_promotion</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-12-01</td>\n",
       "      <td>december_promotion</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2014-12-01</td>\n",
       "      <td>december_promotion</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015-12-01</td>\n",
       "      <td>december_promotion</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ds             holiday  lower_window  upper_window\n",
       "0 2009-12-01  december_promotion             0             0\n",
       "1 2010-12-01  december_promotion             0             0\n",
       "2 2011-12-01  december_promotion             0             0\n",
       "3 2012-12-01  december_promotion             0             0\n",
       "4 2013-12-01  december_promotion             0             0\n",
       "5 2014-12-01  december_promotion             0             0\n",
       "6 2015-12-01  december_promotion             0             0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "promotions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "To continue, we need to log-transform our data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['y'] = np.log(df['y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>67</th>\n",
       "      <td>2015-05-01</td>\n",
       "      <td>13.044650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>2015-06-01</td>\n",
       "      <td>13.013060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>2015-07-01</td>\n",
       "      <td>13.033991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>13.030993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>2015-09-01</td>\n",
       "      <td>12.973671</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ds          y\n",
       "67 2015-05-01  13.044650\n",
       "68 2015-06-01  13.013060\n",
       "69 2015-07-01  13.033991\n",
       "70 2015-08-01  13.030993\n",
       "71 2015-09-01  12.973671"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running Prophet\n",
    "\n",
    "Now, let's set prophet up to begin modeling our data using our ```promotions``` dataframe as part of the forecast\n",
    "\n",
    "Note: Since we are using monthly data, you'll see a message from Prophet saying ```Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.```  This is OK since we are workign with monthly data but you can disable it by using ```weekly_seasonality=True``` in the instantiation of Prophet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n"
     ]
    }
   ],
   "source": [
    "model = Prophet(holidays=promotions)\n",
    "model.fit(df);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've instantiated the model, now we need to build some future dates to forecast into."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>2017-04-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>2017-05-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>2017-06-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>2017-07-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>2017-08-31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ds\n",
       "91 2017-04-30\n",
       "92 2017-05-31\n",
       "93 2017-06-30\n",
       "94 2017-07-31\n",
       "95 2017-08-31"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "future = model.make_future_dataframe(periods=24, freq = 'm')\n",
    "future.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To forecast this future data, we need to run it through Prophet's model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "forecast = model.predict(future)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting forecast dataframe contains quite a bit of data, but we really only care about a few columns.  First, let's look at the full dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>t</th>\n",
       "      <th>trend</th>\n",
       "      <th>seasonal_lower</th>\n",
       "      <th>seasonal_upper</th>\n",
       "      <th>trend_lower</th>\n",
       "      <th>trend_upper</th>\n",
       "      <th>yhat_lower</th>\n",
       "      <th>yhat_upper</th>\n",
       "      <th>december_promotion</th>\n",
       "      <th>december_promotion_lower</th>\n",
       "      <th>december_promotion_upper</th>\n",
       "      <th>yearly</th>\n",
       "      <th>yearly_lower</th>\n",
       "      <th>yearly_upper</th>\n",
       "      <th>seasonal</th>\n",
       "      <th>yhat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>2017-04-30</td>\n",
       "      <td>1.280888</td>\n",
       "      <td>13.045281</td>\n",
       "      <td>0.017746</td>\n",
       "      <td>0.017746</td>\n",
       "      <td>12.921667</td>\n",
       "      <td>13.164843</td>\n",
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       "      <td>13.184197</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.017746</td>\n",
       "      <td>0.017746</td>\n",
       "      <td>0.017746</td>\n",
       "      <td>0.017746</td>\n",
       "      <td>13.063027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>2017-05-31</td>\n",
       "      <td>1.295234</td>\n",
       "      <td>13.046923</td>\n",
       "      <td>0.008715</td>\n",
       "      <td>0.008715</td>\n",
       "      <td>12.917939</td>\n",
       "      <td>13.175036</td>\n",
       "      <td>12.923197</td>\n",
       "      <td>13.182217</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.008715</td>\n",
       "      <td>0.008715</td>\n",
       "      <td>0.008715</td>\n",
       "      <td>0.008715</td>\n",
       "      <td>13.055638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>2017-06-30</td>\n",
       "      <td>1.309116</td>\n",
       "      <td>13.048513</td>\n",
       "      <td>0.026016</td>\n",
       "      <td>0.026016</td>\n",
       "      <td>12.909993</td>\n",
       "      <td>13.184642</td>\n",
       "      <td>12.932721</td>\n",
       "      <td>13.211859</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.026016</td>\n",
       "      <td>0.026016</td>\n",
       "      <td>0.026016</td>\n",
       "      <td>0.026016</td>\n",
       "      <td>13.074529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>2017-07-31</td>\n",
       "      <td>1.323461</td>\n",
       "      <td>13.050155</td>\n",
       "      <td>0.000594</td>\n",
       "      <td>0.000594</td>\n",
       "      <td>12.902636</td>\n",
       "      <td>13.196556</td>\n",
       "      <td>12.897856</td>\n",
       "      <td>13.198499</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000594</td>\n",
       "      <td>0.000594</td>\n",
       "      <td>0.000594</td>\n",
       "      <td>0.000594</td>\n",
       "      <td>13.050750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>1.337807</td>\n",
       "      <td>13.051798</td>\n",
       "      <td>-0.027989</td>\n",
       "      <td>-0.027989</td>\n",
       "      <td>12.891882</td>\n",
       "      <td>13.209361</td>\n",
       "      <td>12.855695</td>\n",
       "      <td>13.179740</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.027989</td>\n",
       "      <td>-0.027989</td>\n",
       "      <td>-0.027989</td>\n",
       "      <td>-0.027989</td>\n",
       "      <td>13.023809</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ds         t      trend  seasonal_lower  seasonal_upper  \\\n",
       "91 2017-04-30  1.280888  13.045281        0.017746        0.017746   \n",
       "92 2017-05-31  1.295234  13.046923        0.008715        0.008715   \n",
       "93 2017-06-30  1.309116  13.048513        0.026016        0.026016   \n",
       "94 2017-07-31  1.323461  13.050155        0.000594        0.000594   \n",
       "95 2017-08-31  1.337807  13.051798       -0.027989       -0.027989   \n",
       "\n",
       "    trend_lower  trend_upper  yhat_lower  yhat_upper  december_promotion  \\\n",
       "91    12.921667    13.164843   12.939614   13.184197                 0.0   \n",
       "92    12.917939    13.175036   12.923197   13.182217                 0.0   \n",
       "93    12.909993    13.184642   12.932721   13.211859                 0.0   \n",
       "94    12.902636    13.196556   12.897856   13.198499                 0.0   \n",
       "95    12.891882    13.209361   12.855695   13.179740                 0.0   \n",
       "\n",
       "    december_promotion_lower  december_promotion_upper    yearly  \\\n",
       "91                       0.0                       0.0  0.017746   \n",
       "92                       0.0                       0.0  0.008715   \n",
       "93                       0.0                       0.0  0.026016   \n",
       "94                       0.0                       0.0  0.000594   \n",
       "95                       0.0                       0.0 -0.027989   \n",
       "\n",
       "    yearly_lower  yearly_upper  seasonal       yhat  \n",
       "91      0.017746      0.017746  0.017746  13.063027  \n",
       "92      0.008715      0.008715  0.008715  13.055638  \n",
       "93      0.026016      0.026016  0.026016  13.074529  \n",
       "94      0.000594      0.000594  0.000594  13.050750  \n",
       "95     -0.027989     -0.027989 -0.027989  13.023809  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We really only want to look at yhat, yhat_lower and yhat_upper, so we can do that with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>2017-04-30</td>\n",
       "      <td>13.063027</td>\n",
       "      <td>12.939614</td>\n",
       "      <td>13.184197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>2017-05-31</td>\n",
       "      <td>13.055638</td>\n",
       "      <td>12.923197</td>\n",
       "      <td>13.182217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>2017-06-30</td>\n",
       "      <td>13.074529</td>\n",
       "      <td>12.932721</td>\n",
       "      <td>13.211859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>2017-07-31</td>\n",
       "      <td>13.050750</td>\n",
       "      <td>12.897856</td>\n",
       "      <td>13.198499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>2017-08-31</td>\n",
       "      <td>13.023809</td>\n",
       "      <td>12.855695</td>\n",
       "      <td>13.179740</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ds       yhat  yhat_lower  yhat_upper\n",
       "91 2017-04-30  13.063027   12.939614   13.184197\n",
       "92 2017-05-31  13.055638   12.923197   13.182217\n",
       "93 2017-06-30  13.074529   12.932721   13.211859\n",
       "94 2017-07-31  13.050750   12.897856   13.198499\n",
       "95 2017-08-31  13.023809   12.855695   13.179740"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Prophet results\n",
    "\n",
    "Prophet has a plotting mechanism called ```plot```.  This plot functionality draws the original data (black dots), the model (blue line) and the error of the forecast (shaded blue area)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AXAkGyERERFRTfomFV4MsYEu14L19Fxu3UGLREtagCYGI4YVkpiuDKXu1lLXcoOaZZscA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\nyEREREREg/xfAwK1tEWySyAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa426b9e310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.plot(forecast);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Personally, I'm not a fan of this visualization but I'm not going to build my own...you can see how I do that here:\n",
    "https://github.com/urgedata/pythondata/blob/master/fbprophet/fbprophet_part_one.ipynb.\n",
    "\n",
    "Additionally, prophet let's us take a at the components of our model, including the holidays. This component plot is an important plot as it lets you see the components of your model including the trend and seasonality (identified in the ```yearly``` pane)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jcKLLnBhDHwQ8Lsr8AN4R85OwbA7Ti7/ES2/CIm7aRE2LZH906PcYRbudmbx3OQsAA4EA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bsEs4mLJ2TwwbHGsHZcKW10848DtVw0PiPsBcQXDLeTZDHxdgSTVyLJniono9ITSLYaHwYAgUSQ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\nCIIgdDL/D9ML/OvvjIRkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa422d7cd50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.plot_components(forecast);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing holidays vs no-holidays forecasts\n",
    "\n",
    "Let's re-run our prophet model without our promotions/holidays for comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n"
     ]
    }
   ],
   "source": [
    "model_no_holiday = Prophet()\n",
    "model_no_holiday.fit(df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>2017-04-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>2017-05-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>2017-06-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>2017-07-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>2017-08-31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ds\n",
       "91 2017-04-30\n",
       "92 2017-05-31\n",
       "93 2017-06-30\n",
       "94 2017-07-31\n",
       "95 2017-08-31"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "future_no_holiday = model_no_holiday.make_future_dataframe(periods=24, freq = 'm')\n",
    "future_no_holiday.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "forecast_no_holiday = model_no_holiday.predict(future)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare the two forecasts now. Note: I doubt there will be much difference in these models due to the small amount of data, but its a good example to see the process. We'll set the indexes and then join the forecast dataframes into a new dataframe called 'compared_df'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "forecast.set_index('ds', inplace=True)\n",
    "forecast_no_holiday.set_index('ds', inplace=True)\n",
    "compared_df = forecast.join(forecast_no_holiday, rsuffix=\"_no_holiday\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are only really insterested in the yhat values, so let's remove all the rest and convert the logged values back to their original scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "compared_df= np.exp(compared_df[['yhat', 'yhat_no_holiday']])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's take the percentage difference and the average difference for the model with holidays vs that without."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>yhat</th>\n",
       "      <th>yhat_no_holiday</th>\n",
       "      <th>diff_per</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ds</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-04-30</th>\n",
       "      <td>471194.800038</td>\n",
       "      <td>471459.411475</td>\n",
       "      <td>-0.056126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-31</th>\n",
       "      <td>467726.135806</td>\n",
       "      <td>468116.264965</td>\n",
       "      <td>-0.083340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-30</th>\n",
       "      <td>476645.649992</td>\n",
       "      <td>476858.563032</td>\n",
       "      <td>-0.044649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-31</th>\n",
       "      <td>465445.198797</td>\n",
       "      <td>465480.406592</td>\n",
       "      <td>-0.007564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31</th>\n",
       "      <td>453073.129385</td>\n",
       "      <td>453067.919980</td>\n",
       "      <td>0.001150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     yhat  yhat_no_holiday  diff_per\n",
       "ds                                                  \n",
       "2017-04-30  471194.800038    471459.411475 -0.056126\n",
       "2017-05-31  467726.135806    468116.264965 -0.083340\n",
       "2017-06-30  476645.649992    476858.563032 -0.044649\n",
       "2017-07-31  465445.198797    465480.406592 -0.007564\n",
       "2017-08-31  453073.129385    453067.919980  0.001150"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compared_df['diff_per'] = 100*(compared_df['yhat'] - compared_df['yhat_no_holiday']) / compared_df['yhat_no_holiday']\n",
    "compared_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.065771316509064426"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compared_df['diff_per'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This isn't an enormous differnece, (<1%) but there is some difference between using holidays and not using holidays.\n",
    "\n",
    "If you know there are holidays or events happening that might help/hurt your forecasting efforts, prophet allows you to easily incorporate them into your modeling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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