How to extract seasonal trends from Prophet

Question

I have been using Prophet from Facebook and so far it has been produced some great results.

Having looked in the docs and googling, there doesn't seem to be an automatic way to extract the seasonal trends from a model as a dataframe or a dict, e.g.:

weekly_trends = { 1 : monday_trend, 2 : tuesday_trend, ... , 7 : sunday_trend } 

yearly_trends = { 1 : day_1_trend, 2 : day_2_trend, ... , 365 : day_365_trend } 

Currently I can extract these out using a more manual way but was just wondering if I had missed something more elegant?

Answer 1

The trained model dataframe has all the seasonal, trend and holidays information. - take a look at its columns. Here's how to look into it in Python:

m = Prophet()
m.fit(ts)
future = m.make_future_dataframe()
forecast = m.predict(future)
print(forecast['weekly'])

Take any 7 days out of that series. That will give you the scale of the additive weekly adjustment for each weekday. Similar for the yearly seasonality.

Answer 2

You can specify daily, weekly, and yearly seasonality during model fit

    m = Prophet(changepoint_prior_scale=0.01, weekly_seasonality=False, holidays=holidays, interval_width=0.90, yearly_seasonality=True, mcmc_samples=300)
    m.add_seasonality(name='weekly', period=7, fourier_order=3)
    m.add_seasonality(name='monthly', period=30.5, fourier_order=8)
    m.fit(X)