PL EN


2014 | 15 | 4 | 611–626
Article title

Functional Regression in Short-Term Prediction of Economic Time Series

Content
Title variants
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.
Year
Volume
15
Issue
4
Pages
611–626
Physical description
Contributors
  • Department of Statistics, Faculty of Management, Cracow University of Economics.
References
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  • BESSE, P., C., CARDOT, H., STEPHENSON, D., B., (2000). Autoregressive Forecasting of Some Functional Climatic Variations, Scandinavian Journal of Statistics, Vol. 27, No. 4, 637–687.
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  • FERRATY, F., (2011). (ed.) Recent Advances in Functional Data Analysis and Related Topic. Physica-Verlag.
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  • HYNDMAN, R. J., KOEHLER, A. B., ORD, J. B., SNYDER, R. D., (2008). Forecasting with exponential smoothing: the state space approach, Springer-Verlag, Berlin.
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  • KOSIOROWSKI, D., MIELCZAREK, D., RYDLEWSKI, J., SNARSKA, M., (2014a). Sparse Methods for Analysis of Sparse Multivariate Data from Big Economic Databases, Statistics in Transition – new series, Vol. 15, No. 1, 111–133.
  • KOSIOROWSKI, D., MIELCZAREK, D., RYDLEWSKI, J., SNARSKA, M., (2014b). Applications of the Functional Data Analysis for Extracting Meaningful Information from Families of Yield Curves and Income Distribution Densities, in Knowledge-Economy-Society Contemporary Tools of Organisational Resources Management, ed. P. Lula, Fundation of the CUE, 309–321.
  • RAMSAY, J. O., HOOKER, G., GRAVES, S., (2009). Functional Data Analysis with R and Matlab, Springer-Verlag, New-York.
  • SHANG, H. L., (2013). ftsa: An R Package for Analyzing Functional Time Series, The R Journal, Vol. 5/1, 65–72.
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Document Type
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.desklight-ca33ba77-d62e-4ee1-91d4-11e9b45a7b51
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