Prophet includes functionality for time series cross validation to measure forecast error using historical data . This is done by selecting cutoff points in the history, and for each of them fitting the model using data only up to that cutoff point. We can then compare the forecasted values to the actual values.
Read moreHow does FB prophet work?
Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects . It works best with time series that have strong seasonal effects and several seasons of historical data.
Read moreWhat is seasonality Prior scale prophet?
Basically seasonality prior scale is setting the standard deviation for the normal distribution . Higher seasonality prior scale means we are setting a wider normal distribution when sampling for the 2*n terms for our fourier. At the end, the yearly components will be simply the fourier generated dot beta.
Read moreHow does Prophet forecasting work?
At its core, the Prophet procedure is an additive regression model with four main components: A piecewise linear or logistic growth curve trend. Prophet automatically detects changes in trends by selecting changepoints from the data . A yearly seasonal component modeled using Fourier series.
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