farforecast: Functional data forecasting through functional principal...

In ftsa: Functional Time Series Analysis

Functional data forecasting through functional principal component autoregression

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", Dmax_value, Pmax_value,
	level = 80, PI = FALSE)

Arguments

object	An object of fds.
h	Forecast horizon.
var_type	Type of multivariate time series forecasting method; see VAR for details.
Dmax_value	Maximum number of components considered.
Pmax_value	Maximum order of VAR model considered.
level	Nominal coverage probability of prediction error bands.
PI	When PI = TRUE, a prediction interval will be given along with the point forecast.

Details

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

2. Fit a multivariate time-series model to the principal component score matrix.

3. Forecast the principal component scores using the fitted multivariate time-series models. The order of VAR is selected optimally via an information criterion.

4. Multiply the forecast principal component scores by estimated 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.

Value

point_fore	Point forecast
order_select	Selected VAR order and number of components
PI_lb	Lower bound of a prediction interval
PI_ub	Upper bound of a prediction interval

Author(s)

Han Lin Shang

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.

J. Klepsch, C. Kl\"uppelberg and T. Wei (2017) "Prediction of functional ARMA processes with an application to traffic data", Econometrics and Statistics, 1, 128-149.

See Also

forecast.ftsm, forecastfplsr

Examples

sqrt_pm10 = sqrt(pm_10_GR$y)
multi_forecast_sqrt_pm10 = farforecast(object = fts(seq(0, 23.5, by = 0.5), sqrt_pm10),
	h = 1, Dmax_value = 5, Pmax_value = 3)

ftsa documentation built on May 29, 2024, 2:47 a.m.