Functional Regression in Short-Term Prediction of Economic Time Series

• Authors: Daniel Kosiorowski
• Languages of publication: EN

Abstracts

EN

We compare four methods of forecasting functional time series including fully functional regression, functional autoregression FAR(1) model, Hyndman & Shang principal component scores forecasting using one-dimensional time series method, and moving functional median. Our comparison methods involve simulation studies as well as analysis of empirical dataset concerning the Internet users behaviours for two Internet services in 2013. Our studies reveal that Hyndman & Shao predicting method outperforms other methods in the case of stationary functional time series without outliers, and the moving functional median induced by Frainman & Muniz depth for functional data outperforms other methods in the case of smooth departures from stationarity of the time series as well as in the case of functional time series containing outliers.

Keywords

EN

functional data analysis; functional time series; prediction

• Publisher: Główny Urząd Statystyczny
• Journal: Statistics in Transition new series
• Year: 2014
• Volume: 15
• Issue: 4
• Pages: 611–626

Contributors

author

Daniel Kosiorowski

• Department of Statistics, Faculty of Management, Cracow University of Economics.

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