ftsmweightselect: Selection of the weight parameter used in the weighted...

In ftsa: Functional Time Series Analysis

Selection of the weight parameter used in the weighted functional time series model.

Description

The geometrically decaying weights are used to estimate the mean curve and functional principal components, where more weights are assigned to the more recent data than the data from the distant past.

Usage

ftsmweightselect(data, ncomp = 6, ntestyear, errorcriterion = c("mae", "mse", "mape"))

Arguments

data	An object of class fts.
ncomp	Number of components.
ntestyear	Number of holdout observations used to assess the forecast accuracy.
errorcriterion	Error measure.

Details

The data set is split into a fitting period and forecasting period. Using the data in the fitting period, we compute the one-step-ahead forecasts and calculate the forecast error. Then, we increase the fitting period by one, and carry out the same forecasting procedure until the fitting period covers entire data set. The forecast accuracy is determined by the averaged forecast error across the years in the forecasting period. By using an optimization algorithm, we select the optimal weight parameter that would result in the minimum forecast error.

Value

Optimal weight parameter.

Note

Can be computational intensive, as it takes about half-minute to compute. For example, ftsmweightselect(ElNinosmooth, ntestyear = 1).

Author(s)

Han Lin Shang

References

R. J. Hyndman and H. L. Shang (2009) "Forecasting functional time series (with discussion)", Journal of the Korean Statistical Society, 38(3), 199-221.

See Also

ftsm, forecast.ftsm

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