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fasster 0.2.0

Fast Additive Switching of Seasonality, Trend and Exogenous Regressors (FASSTER) is a state space model designed for forecasting time series with multiple seasonal patterns. The model extends traditional state space models by introducing a switching component to the measurement equation, enabling flexible modeling of complex seasonal patterns, and time series dynamics with rapid structural changes.

• FASSTER model implementation:

• Model specification: Flexible formula interface supporting:

  • trend() for polynomial trends
  • season() for seasonal factors
  • fourier() for trigonometric seasonal terms
  • ARMA() for autoregressive moving average components
  • xreg() for exogenous regressors
  • %S% switching operator for group-specific model structures
  • %?% conditional operator for time-varying components

• Model methods: Full integration with the fable framework:

  • fitted() and residuals() for model diagnostics
  • augment() for augmenting data with model estimates
  • tidy() for extracting coefficients (initial state estimates)
  • glance() for model summary statistics (AIC, BIC, log-likelihood)
  • report() for displaying estimated state and observation variances
  • components() for decomposing fitted values into trend and seasonal components
  • forecast() for generating predictions
  • interpolate() for filling missing values
  • refit() for applying a fitted model to new data with optional re-estimation
  • stream() for extending models with new observations

• Heuristic estimation: Model parameters are estimated using a heuristic approach based on filtering and smoothing to obtain initial state parameters and variance estimates.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.