fasster (version 0.2.0)

stream.FASSTER: Stream new data through a FASSTER model

Description

Extends a fitted FASSTER model by filtering new observations through the existing state space model. The model's states and parameters are updated sequentially as new data arrives, allowing for online learning without refitting from scratch.

Usage

# S3 method for FASSTER
stream(object, new_data, specials = NULL, ...)

Value

An updated FASSTER model object with:

• Extended state estimates incorporating the new data

• Updated model variance

• Appended fitted values and residuals

• Updated DLM components for future forecasting

Arguments

object

A fitted FASSTER model object

new_data

A tsibble containing new observations to stream through the model

specials

A list of special terms (switching variables, etc.) parsed from the model formula

...

Additional arguments (currently unused)

Details

The streaming process:

1. Constructs the design matrix from new data

2. Applies the Kalman filter to sequentially update states

3. Updates model variance based on all residuals

4. Prepares the model for subsequent forecasting or streaming

Examples

library(tsibble)
library(fasster)

# Fit initial model on training data
fit <- as_tsibble(head(USAccDeaths, -12)) |>
  model(fasster = FASSTER(value ~ trend() + season("year")))
tidy(fit)
tail(fitted(fit), 20)

# Stream new data through the model
fit_updated <- fit |>
  stream(as_tsibble(tail(USAccDeaths, 12)))
tidy(fit_updated)
tail(fitted(fit_updated), 20)