Produces forecasts from a trained FASSTER model using the Kalman filter framework. This method generates point forecasts and prediction intervals by propagating the state space forward through time.
# S3 method for FASSTER
forecast(object, new_data, specials = NULL, ...)A distribution vector (from the distributional package) containing normal distributions with forecasted means and standard errors for each future time point. This integrates with fable's forecast distribution structure.
A trained FASSTER model object.
A tsibble containing future time points to forecast. Must be regularly spaced and contain any required exogenous regressors.
A list of special formulations generated from the model formula, used to construct the design matrix for time-varying components.
Additional arguments (currently unused).
The forecast method implements a Kalman filter to propagate the state space model forward in time:
For each forecast horizon, constructs time-varying system matrices
(FF, GG, V, W) using exogenous variables from new_data
Computes the state forecast: \(a_{t+1} = G_t a_t\)
Computes the state covariance: \(R_{t+1} = G_t R_t G_t' + W_t\)
Computes the observation forecast: \(f_t = F_t a_{t+1}\)
Computes the forecast variance: \(Q_t = F_t R_{t+1} F_t' + V_t\)
The method handles switching components by matching exogenous variables
in new_data with the model's design matrix, adding zero columns
for any missing levels.