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Application of wavelet decomposition in time-series forecasting

Author

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  • Zhang, Keyi
  • Gençay, Ramazan
  • Ege Yazgan, M.

Abstract

Observed time series data can exhibit different components, such as trends, seasonality, and jumps, which are characterized by different coefficients in their respective data generating processes. Therefore, fitting a given time series model to aggregated data can be time consuming and may lead to a loss of forecasting accuracy. In this paper, coefficients for variable components in estimations are generated based on wavelet-based multiresolution analyses. Thus, the accuracy of forecasts based on aggregate data should be improved because the constraint of equality among the model coefficients for all data components is relaxed.

Suggested Citation

  • Zhang, Keyi & Gençay, Ramazan & Ege Yazgan, M., 2017. "Application of wavelet decomposition in time-series forecasting," Economics Letters, Elsevier, vol. 158(C), pages 41-46.
    • Handle: RePEc:eee:ecolet:v:158:y:2017:i:c:p:41-46
    DOI: 10.1016/j.econlet.2017.06.010
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    References listed on IDEAS

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    Cited by:

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    3. Manuel M. F. Martins & Fabio Verona, 2021. "Inflation Dynamics and Forecast: Frequency Matters," CEF.UP Working Papers 2101, Universidade do Porto, Faculdade de Economia do Porto.
    4. repec:zbw:bofrdp:2021_008 is not listed on IDEAS
    5. Manuel M. F. Martins & Fabio Verona, 2021. "Inflation Dynamics and Forecast: Frequency Matters," CEF.UP Working Papers 2101, Universidade do Porto, Faculdade de Economia do Porto.
    6. Vera Ivanyuk, 2022. "Methodology for Constructing an Experimental Investment Strategy Formed in Crisis Conditions," Economies, MDPI, vol. 10(12), pages 1-19, December.
    7. Faria, Gonçalo & Verona, Fabio, 2018. "The equity risk premium and the low frequency of the term spread," Research Discussion Papers 7/2018, Bank of Finland.
    8. Gonçalo Faria & Fabio Verona, 2021. "Time-frequency forecast of the equity premium," Quantitative Finance, Taylor & Francis Journals, vol. 21(12), pages 2119-2135, December.
    9. repec:zbw:bofrdp:2020_006 is not listed on IDEAS
    10. Manuel M. F. Martins & Fabio Verona, 2020. "Forecasting Inflation with the New Keynesian Phillips Curve: Frequency Matters," CEF.UP Working Papers 2001, Universidade do Porto, Faculdade de Economia do Porto.
    11. Kim C. Raath & Katherine B. Ensor, 2023. "Wavelet-L2E Stochastic Volatility Models: an Application to the Water-Energy Nexus," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 150-176, May.
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    13. Nurul Aityqah Yaacob & Jamil J. Jaber & Dharini Pathmanathan & Sadam Alwadi & Ibrahim Mohamed, 2021. "Hybrid of the Lee-Carter Model with Maximum Overlap Discrete Wavelet Transform Filters in Forecasting Mortality Rates," Mathematics, MDPI, vol. 9(18), pages 1-11, September.
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    More about this item

    Keywords

    Wavelet decomposition; Combining forecasts; Reconciling forecasts; Hierarchical time series;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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