tidy() now reports extra regressor estimates as coefficients on the original data scale (additive regressors in units of the response, multiplicative regressors as proportional effects), rather than prophet's internal scaled parameters. Other terms remain on the internal scale, as documented in ?tidy.fbl_prophet.forecast() no longer forwards ... to prophet::predictive_samples(), which doesn't accept additional arguments. ... is now documented as unused.generate() for prophet models (#4), simulating sample paths (trend including future changepoints, seasonality, holidays and regressors) with normally distributed or, with bootstrap = TRUE, resampled-residual observation noise. Works in-sample and out-of-sample, including with MCMC estimation and transformed responses.refit() for prophet models (#30). By default the estimated parameters are kept and the fitted values, residuals and components are recomputed for the new data; reestimate = TRUE fits the model again on the new data using the original estimation arguments. Prophet cannot be updated incrementally, so there is no stream() method.growth() now supports type = "flat", for a constant trend without changepoints.holiday() gains a country argument to include prophet's built-in country holidays (via prophet::add_country_holidays()), with or without a holidays table. tidy() now names (and orders) holiday terms from the fitted model, so country holidays and repeated holiday dates are supported.season() gains a condition argument (a bare logical column name) for conditional seasonality (via prophet::add_seasonality(condition.name = )). The column must also be present in new_data when forecasting.prophet() now documents and supports the mcmc.samples and backend arguments (passed to prophet::prophet()) for MCMC estimation and choosing the "rstan" or "cmdstanr" Stan backend. tidy() and glance() summarise MCMC draws by their posterior means.forecast() now supports times = 0, which returns point forecasts (as degenerate distributions) without simulating sample paths. The times argument is now documented, including its speed and memory trade-off (#26).components() now includes the holidays, extra_regressors_additive and extra_regressors_multiplicative terms when they are present in the model.stan.fit), reducing model size (#26).log(x) or I(x^2)) are now supported, using make.names() to name them in the model and in tidy() (#32).?prophet and the introduction vignette.uncertainty.samples = 0 (previously the argument was misspelt and ignored), so fitting no longer computes unused uncertainty samples.forecast() (#33).Small patch for compatibility with fabletools v1.0.0.
forecast(), components(), fitted(), residuals().