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Time series forecasting with a prior wavelet-based denoising step

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  • Milan Bašta

Abstract

We provide an extensive study assessing whether a prior wavelet-based denoising step enhances the forecast accuracy of standard forecasting models. Many combinations of attribute values of the thresholding (denoising) algorithm are explored together with several traditional forecasting models used in economic time series forecasting. The results are evaluated using M3 competition yearly time series. We conclude that the performance of a forecasting model combined with the prior denoising step is generally not recommended, which implies that a straightforward generalisation of some of the results available in the literature (which found the denoising step to be beneficial) is not possible. Even if cross-validation is used to select the value of the threshold, a superior performance of the forecasting model with the prior denoising step does not generally follow.

Suggested Citation

  • 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.
    • Handle: RePEc:prg:jnlaop:v:2018:y:2018:i:1:id:592:p:5-24
    DOI: 10.18267/j.aop.592
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    References listed on IDEAS

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    1. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    2. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    3. 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.
    4. Marco Gallegati & Willi Semmler (ed.), 2014. "Wavelet Applications in Economics and Finance," Dynamic Modeling and Econometrics in Economics and Finance, Springer, edition 127, number 978-3-319-07061-2, July-Dece.
    5. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
    6. Ferbar, Liljana & Creslovnik, David & Mojskerc, Blaz & Rajgelj, Martin, 2009. "Demand forecasting methods in a supply chain: Smoothing and denoising," International Journal of Production Economics, Elsevier, vol. 118(1), pages 49-54, March.
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    More about this item

    Keywords

    wavelets; noise; evaluating forecasts; automatic forecasting;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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