ftsa (version 4.4)

farforecast: Functional data forecasting through functional principal component regression

Description

The coefficients from the fitted object are forecasted using a multivariate time-series forecasting method. The forecast coefficients are then multiplied by the functional principal components to obtain a forecast curve.

Usage

farforecast(object, h = 10, var_type = "const", level = 80, PI = FALSE)

Value

point_forePoint forecastPI_lbLower bound of a prediction intervalPI_ubUpper bound of a prediction interval

Details

1. Decompose the smooth curves via a functional principal component analysis.

2. Fit a multivariate time-series model to each of the principal component scores.

3. Forecast the principal component scores using the fitted multivariate time-series models.

4. Multiply the forecast principal component scores by fixed principal components to obtain forecasts of $f_{n+h}(x)$.

5. Prediction intervals are constructed by taking quantiles of the one-step-ahead forecast errors.

References

A. Aue, D. D. Norinho and S. Hormann (2015) "On the prediction of stationary functional time series", Journal of the American Statistical Association, 110(509), 378-392.

See Also

forecast.ftsm

Examples

ElNino_subset = extract(ElNino,"time",timeorder = 1967:2006)
ex = farforecast(ftsm(ElNino_subset), PI=TRUE)