FORECASTING VIA WAVELET SMOOTHING
DOI:
https://doi.org/10.12775/AUNC_ZARZ.2012.006Keywords
wavelet forecasting, nonparametric signal estimation, wavelet smoothingAbstract
In the paper we discuss existing techniques for univariate time series forecasting based on the wavelet transform and introduce also a new method of forecasting with wavelets. The new approach is based on a nonparametric estimation of a stochastic signal via a wavelet smoothing. The method can be thought of as a wavelet variant of the exponential smoothing, which is, however, much more universal, being at the same time relatively computationally efficient. Our empirical verification based on 17 time series from the M3-JIF-Competition database provides very promising results, confirming the practical relevance of the suggested approach.References
Alrumaih R. M., Al-Fawzan M. A. (2002), Time Series Forecasting Using Wavelet Denoising, Journal of King Saudi University, Engineering Sciences, 14, 221–234.
Arino M. (1995), Time Series Forecasts via Wavelets: An Application to Car Sales in the Spanish Market, Discussion Paper No. 95–30, Institute of Statistics and Decision Sciences, Duke University.
Augustyniak P. (2003), Transformacje falkowe w zastosowaniach elektrodiagnostycznych, Uczelniane Wydawnictwa Naukowo-Dydaktyczne, AGH, Kraków.
Bruzda J. (2011), Wavelet Analysis in Economic Applications, monografia w przygotowaniu. Chen H., Vidacovic B., Mavris D. (2004), Multiscale Forecasting Method using AR MAX Models, Biomedical Engineering Technical Report No. 30/2004, Georgia Institute of Technology.
Conejo A. J., Plazas M. A., Espínola R., Molina A. B. (2005), Day-Ahead Electricity Price Forecasting Using the Wavelet Transform and ARI MA Models, IEEE Transactions on Power Systems, 20, 1035–1042. DOI: http://dx.doi.org/10.1109/TPWRS.2005.846054
Ferbar L., Čreslovnik D., Mojškerc B., Rajgelj M. (2009), Demand Forecasting Methods in a Supply Chain: Smoothing and Denoising, International Journal of Production Economics, 118, 49–54. DOI: http://dx.doi.org/10.1016/j.ijpe.2008.08.042
Fernandez V. (2008), Traditional versus Novel Forecasting Techniques: How Much do We Gain?, Journal of Forecasting, 27, 637–648. DOI: http://dx.doi.org/10.1002/for.1066
Fryźlewicz P., Van Bellegem S., von Sachs R. (2003), Forecasting Nonstationary Time Series by Wavelet Process Modelling, Annals of the Institute of Statistical Mathematics, 55, 737–764.
Kaboudan M. (2005), Extended Daily Exchange Rates Forecasts Using Wavelet Temporal Resolution, New Mathematics and Natural Computation, 1, 79–107. DOI: http://dx.doi.org/10.1142/S1793005705000056
Makridakis S., Hibon M. (2000), The M3-Competition: Results, Conclusions and Implications, International Journal of Forecasting, 16, 451–476. DOI: http://dx.doi.org/10.1016/S0169-2070(00)00057-1
Minu K. K., Lineesh M. C., Jessy John C. (2010), Wavelet Neural Networks for Nonlinear Time Series Analysis, Applied Mathematical Sciences, 4, 2485–2495.
Nason G. P. (2008), Wavelet Methods in Statistics with R, Springer-Business Media, New York. DOI: http://dx.doi.org/10.1007/978-0-387-75961-6
Percival D. B., Walden A. T. (2000), Wavelet Methods for Time Series Analysis, Cambridge University Press, Cambridge.
Renaud O., Starck J.-L., Murtagh F. (2002), Wavelet-based Forecasting of Short and Long Memory Time Series, Working Paper No. 2002.04, University of Geneva.
Schlüter S., Deuschle C. (2010), Using Wavelets for Time Series Forecasting – Does it Pay Off?, Diskussionspapier No. 4/2010, Institut für Wirtschaftspolitik und Quantitative Wirtschaftsforschung, Friedrich-Alexander-Universität.
Wong H., Ip W.-C., Xie Z., Lui X. (2003), Modelling and Forecasting by Wavelets, and the Application to Exchange Rates, Journal of Applied Statistics, 30, 537v553. DOI: http://dx.doi.org/10.1080/0266476032000053664
Downloads
Published
How to Cite
Issue
Section
Stats
Number of views and downloads: 423
Number of citations: 0