ftsa (version 6.4)

dynamic_FLR: Dynamic updates via functional linear regression

Description

A functional linear regression is used to address the problem of dynamic updating, when partial data in the most recent curve are observed.

Usage

dynamic_FLR(dat, newdata, holdoutdata, order_k_percent = 0.9, order_m_percent = 0.9, 
    pcd_method = c("classical", "M"), robust_lambda = 2.33, bootrep = 100, 
    	pointfore, level = 80)

Value

update_forecast

Updated forecasts.

holdoutdata

Holdout sample.

err

Forecast errors.

order_k

Number of principal components in the first block of functions.

order_m

Number of principal components in the second block of functions.

update_comb

Bootstrapped forecasts for the dynamically updating time period.

update_comb_lb_ub

By taking corresponding quantiles, obtain lower and upper prediction bounds.

err_boot

Bootstrapped in-sample forecast error for the dynamically updating time period.

Arguments

dat

An object of class sfts.

newdata

A data vector of newly arrived observations.

holdoutdata

A data vector of holdout sample to evaluate point forecast accuracy.

order_k_percent

Select the number of components that explains at least 90 percent of the total variation.

order_m_percent

Select the number of components that explains at least 90 percent of the total variation.

pcd_method

Method to use for principal components decomposition. Possibilities are "M", "rapca" and "classical".

robust_lambda

Tuning parameter in the two-step robust functional principal component analysis, when pcdmethod = "M".

bootrep

Number of bootstrap samples.

pointfore

If pointfore = TRUE, point forecasts are produced.

level

Nominal coverage probability.

Author

Han Lin Shang

Details

This function is designed to dynamically update point and interval forecasts, when partial data in the most recent curve are observed.

References

H. Shen and J. Z. Huang (2008) "Interday forecasting and intraday updating of call center arrivals", Manufacturing and Service Operations Management, 10(3), 391-410.

H. Shen (2009) "On modeling and forecasting time series of curves", Technometrics, 51(3), 227-238.

H. L. Shang and R. J. Hyndman (2011) "Nonparametric time series forecasting with dynamic updating", Mathematics and Computers in Simulation, 81(7), 1310-1324.

J-M. Chiou (2012) "Dynamical functional prediction and classification with application to traffic flow prediction", Annals of Applied Statistics, 6(4), 1588-1614.

H. L. Shang (2013) "Functional time series approach for forecasting very short-term electricity demand", Journal of Applied Statistics, 40(1), 152-168.

H. L. Shang (2015) "Forecasting Intraday S&P 500 Index Returns: A Functional Time Series Approach", Journal of Forecasting, 36(7), 741-755.

H. L. Shang (2017) "Functional time series forecasting with dynamic updating: An application to intraday particulate matter concentration", Econometrics and Statistics, 1, 184-200.

See Also

dynupdate

Examples

dynamic_FLR_point = dynamic_FLR(dat = ElNino_ERSST_region_1and2$y[,1:68], 
	newdata = ElNino_ERSST_region_1and2$y[1:4,69], 
	holdoutdata = ElNino_ERSST_region_1and2$y[5:12,69], pointfore = TRUE)

dynamic_FLR_interval = dynamic_FLR(dat = ElNino_ERSST_region_1and2$y[,1:68], 
	newdata = ElNino_ERSST_region_1and2$y[1:4,69], 
	holdoutdata = ElNino_ERSST_region_1and2$y[5:12,69], pointfore = FALSE)