

In [1]:

    
%load_ext rpy2.ipython
%matplotlib inline
from fbprophet import Prophet
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import logging
logging.getLogger('fbprophet').setLevel(logging.ERROR)
import warnings
warnings.filterwarnings("ignore")



In [2]:

    
%%R
library(prophet)

By default Prophet fits additive seasonalities, meaning the effect of the seasonality is added to the trend to get the forecast. This time series of the number of air passengers is an example of when additive seasonality does not work:



In [3]:

    
%%R -w 10 -h 6 -u in
df <- read.csv('../examples/example_air_passengers.csv')
m <- prophet(df)
future <- make_future_dataframe(m, 50, freq = 'm')
forecast <- predict(m, future)
plot(m, forecast)









    





Initial log joint probability = -2.46502
Optimization terminated normally: 
  Convergence detected: absolute parameter change was below tolerance



In [4]:

    
df = pd.read_csv('../examples/example_air_passengers.csv')
m = Prophet()
m.fit(df)
future = m.make_future_dataframe(50, freq='MS')
forecast = m.predict(future)
fig = m.plot(forecast)

This time series has a clear yearly cycle, but the seasonality in the forecast is too large at the start of the time series and too small at the end. In this time series, the seasonality is not a constant additive factor as assumed by Prophet, rather it grows with the trend. This is multiplicative seasonality.

Prophet can model multiplicative seasonality by setting seasonality_mode='multiplicative' in the input arguments:



In [5]:

    
%%R -w 10 -h 6 -u in
m <- prophet(df, seasonality.mode = 'multiplicative')
forecast <- predict(m, future)
plot(m, forecast)









    





Initial log joint probability = -2.46502
Optimization terminated normally: 
  Convergence detected: relative gradient magnitude is below tolerance



In [6]:

    
m = Prophet(seasonality_mode='multiplicative')
m.fit(df)
forecast = m.predict(future)
fig = m.plot(forecast)

The components figure will now show the seasonality as a percent of the trend:



In [7]:

    
%%R -w 9 -h 6 -u in
prophet_plot_components(m, forecast)



In [8]:

    
fig = m.plot_components(forecast)

With seasonality_mode='multiplicative', holiday effects will also be modeled as multiplicative. Any added seasonalities or extra regressors will by default use whatever seasonality_mode is set to, but can be overriden by specifying mode='additive' or mode='multiplicative' as an argument when adding the seasonality or regressor.

For example, this block sets the built-in seasonalities to multiplicative, but includes an additive quarterly seasonality and an additive regressor:



In [9]:

    
%%R
m <- prophet(seasonality.mode = 'multiplicative')
m <- add_seasonality(m, 'quarterly', period = 91.25, fourier.order = 8, mode = 'additive')
m <- add_regressor(m, 'regressor', mode = 'additive')



In [10]:

    
m = Prophet(seasonality_mode='multiplicative')
m.add_seasonality('quarterly', period=91.25, fourier_order=8, mode='additive')
m.add_regressor('regressor', mode='additive')









    Out[10]:





<fbprophet.forecaster.Prophet at 0x7f2f9db52d10>

Additive and multiplicative extra regressors will show up in separate panels on the components plot.