Lok at time series of daily page views for the Wikipedia page for Peyton Manning. The csv is available here
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#wp_R_dataset_url = 'https://github.com/facebookincubator/prophet/blob/master/examples/example_wp_R.csv'
wp_peyton_manning_filename = '../datasets/example_wp_peyton_manning.csv'
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%matplotlib inline
from fbprophet import Prophet
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
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df = pd.read_csv(wp_peyton_manning_filename)
# transform to log scale
df['y']=np.log(df['y'])
m = Prophet()
m.fit(df)
future = m.make_future_dataframe(periods=366)
If you have holidays 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.
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 included 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.
Here we create a dataframe that includes the dates of all of Peyton Manning’s playoff appearances:
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playoffs = pd.DataFrame({
'holiday': 'playoff',
'ds': pd.to_datetime(['2008-01-13', '2009-01-03', '2010-01-16',
'2010-01-24', '2010-02-07', '2011-01-08',
'2013-01-12', '2014-01-12', '2014-01-19',
'2014-02-02', '2015-01-11', '2016-01-17',
'2016-01-24', '2016-02-07']),
'lower_window': 0,
'upper_window': 1,
})
superbowls = pd.DataFrame({
'holiday': 'superbowl',
'ds': pd.to_datetime(['2010-02-07', '2014-02-02', '2016-02-07']),
'lower_window': 0,
'upper_window': 1,
})
holidays = pd.concat((playoffs, superbowls))
holidays.head()
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Above we have include 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.
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:
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m = Prophet(holidays=holidays)
forecast = m.fit(df).predict(future)
forecast dataframe:
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forecast[(forecast['playoff']+forecast['superbowl']).abs() > 0][['ds','playoff','superbowl']][-10:]
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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:
Whereas decreasing it will make the trend less flexible
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m.plot_components(forecast)
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m = Prophet(holidays=holidays, holidays_prior_scale=1).fit(df)
forecast = m.predict(future)
forecast[(forecast['playoff'] + forecast['superbowl']).abs() > 0][
['ds', 'playoff', 'superbowl']][-10:]
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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.
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