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Wavelet-based forecasting of ARIMA time series - an empirical comparison of different methods

Author

Listed:
  • Stephan Schlueter

    (University of Erlangen-Nuremberg)

  • Carola Deuschle

    (University of Erlangen-Nuremberg)

Abstract

By means of wavelet transform, an ARIMA time series can be split into different frequency com- ponents. In doing so, one is able to identify relevant patters within this time series, and there are different ways to utilize this feature to improve existing time series forecasting methods. However, despite a considerable amount of literature on the topic, there is hardly any work that compares the different wavelet-based methods with each other. In this paper, we try to close this gap. We test various wavelet-based methods on four data sets, each with its own character- istics. Eventually, we come to the conclusion that using wavelets does improve forecasting qual- ity, especially for time horizons longer than one-day-ahead. However, there is no single superior method: either wavelet-based denoising or wavelet-based time series decomposition is best. Performance depends on the data set as well as the forecasting time horizon.

Suggested Citation

  • Stephan Schlueter & Carola Deuschle, 2014. "Wavelet-based forecasting of ARIMA time series - an empirical comparison of different methods," Managerial Economics, AGH University of Science and Technology, Faculty of Management, vol. 15(1), pages 107-131.
    • Handle: RePEc:agh:journl:v:15:y:2014:i:1:p:107-131
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    Cited by:

    1. Joanna Bruzda, 2020. "The wavelet scaling approach to forecasting: Verification on a large set of Noisy data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 353-367, April.
    2. Kojić, Milena & Schlüter, Stephan & Mitić, Petar & Hanić, Aida, 2022. "Economy-environment nexus in developed European countries: Evidence from multifractal and wavelet analysis," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).

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