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Forecasting via Wavelet Denoising: The Random Signal Case

In: Wavelet Applications in Economics and Finance

Author

Listed:
  • Joanna Bruzda

    (Nicolaus Copernicus University)

Abstract

In the paper we evaluate the usability of certain wavelet-based methods of signal estimation for forecasting economic time series. We concentrate on extracting stochastic signals embedded in white noise with the help of wavelet scaling based on the non-decimated version of the discrete wavelet transform. The methods used here can be thought of as a type of smoothing, with weights depending on the frequency content of the examined processes. Both our simulation study and empirical examination based on time series from the M3-JIF Competition database show that the suggested forecasting procedures may be useful in economic applications.

Suggested Citation

  • Joanna Bruzda, 2014. "Forecasting via Wavelet Denoising: The Random Signal Case," Dynamic Modeling and Econometrics in Economics and Finance, in: Marco Gallegati & Willi Semmler (ed.), Wavelet Applications in Economics and Finance, edition 127, pages 187-225, Springer.
    • Handle: RePEc:spr:dymchp:978-3-319-07061-2_9
    DOI: 10.1007/978-3-319-07061-2_9
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    Cited by:

    1. Antonis A. Michis, 2022. "Multiscale Partial Correlation Clustering of Stock Market Returns," JRFM, MDPI, vol. 15(1), pages 1-22, January.
    2. Milan Bašta, 2018. "Time series forecasting with a prior wavelet-based denoising step," Acta Oeconomica Pragensia, Prague University of Economics and Business, vol. 2018(1), pages 5-24.

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