forecast.hdfpca: Forecasting via a high-dimensional functional principal...

In ftsa: Functional Time Series Analysis

Forecasting via a high-dimensional functional principal component regression

Description

Forecast high-dimensional functional principal component model.

Usage

## S3 method for class 'hdfpca'
forecast(object, h = 3, level = 80, B = 50, ...)

Arguments

object	An object of class 'hdfpca'
h	Forecast horizon
level	Prediction interval level, the default is 80 percent
B	Number of bootstrap replications
...	Other arguments passed to forecast routine.

Details

The low-dimensional factors are forecasted with autoregressive integrated moving average (ARIMA) models separately. The forecast functions are then calculated using the forecast factors. Bootstrap prediction intervals are constructed by resampling from the forecast residuals of the ARIMA models.

Value

forecast	A list containing the h-step-ahead forecast functions for each population
upper	Upper confidence bound for each population
lower	Lower confidence bound for each population

Author(s)

Y. Gao and H. L. Shang

References

Y. Gao, H. L. Shang and Y. Yang (2018) High-dimensional functional time series forecasting: An application to age-specific mortality rates, Journal of Multivariate Analysis, forthcoming.

See Also

hdfpca, hd_data

Examples

## Not run: 
hd_model = hdfpca(hd_data, order = 2, r = 2)
hd_model_fore = forecast.hdfpca(object = hd_model, h = 1)

## End(Not run)

ftsa documentation built on Sept. 11, 2023, 5:09 p.m.