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Using wavelets for time series forecasting: Does it pay off?

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  • Schlüter, Stephan
  • Deuschle, Carola

Abstract

By means of wavelet transform a time series can be decomposed into a time dependent sum of frequency components. As a result we are able to capture seasonalities with time-varying period and intensity, which nourishes the belief that incorporating the wavelet transform in existing forecasting methods can improve their quality. The article aims to verify this by comparing the power of classical and wavelet based techniques on the basis of four time series, each of them having individual characteristics. We find that wavelets do improve the forecasting quality. Depending on the data's characteristics and on the forecasting horizon we either favour a denoising step plus an ARIMA forecast or an multiscale wavelet decomposition plus an ARIMA forecast for each of the frequency components.

Suggested Citation

  • Schlüter, Stephan & Deuschle, Carola, 2010. "Using wavelets for time series forecasting: Does it pay off?," FAU Discussion Papers in Economics 04/2010, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    • Handle: RePEc:zbw:iwqwdp:042010
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    References listed on IDEAS

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    3. Piotr Fryzlewicz & Sébastien Bellegem & Rainer Sachs, 2003. "Forecasting non-stationary time series by wavelet process modelling," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 55(4), pages 737-764, December.
    4. C. W. J. Granger & Roselyne Joyeux, 1980. "An Introduction To Long‐Memory Time Series Models And Fractional Differencing," Journal of Time Series Analysis, Wiley Blackwell, vol. 1(1), pages 15-29, January.
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    Cited by:

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    2. Nowotarski, Jakub & Tomczyk, Jakub & Weron, Rafał, 2013. "Robust estimation and forecasting of the long-term seasonal component of electricity spot prices," Energy Economics, Elsevier, vol. 39(C), pages 13-27.
    3. Kriechbaumer, Thomas & Angus, Andrew & Parsons, David & Rivas Casado, Monica, 2014. "An improved wavelet–ARIMA approach for forecasting metal prices," Resources Policy, Elsevier, vol. 39(C), pages 32-41.
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    5. Schnitzlein, Daniel D., 2012. "How important is cultural background for the level of intergenerational mobility?," Economics Letters, Elsevier, vol. 114(3), pages 335-337.
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    More about this item

    Keywords

    Forecasting; Wavelets; ARIMA; Denoising; Multiscale Analysis;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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