Tag: Neural Prophet

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.

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.

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.