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.
Usage
# S3 method for class 'FASSTER'
forecast(object, new_data, specials = NULL, ...)Arguments
- object
A trained FASSTER model object.
- new_data
A tsibble containing future time points to forecast. Must be regularly spaced and contain any required exogenous regressors.
- specials
A list of special formulations generated from the model formula, used to construct the design matrix for time-varying components.
- ...
Additional arguments (currently unused).
Value
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.
Details
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_dataComputes 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.