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Forecasting Time Series via Discrete Wavelet Transform

Author

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  • Miguel A. Ario

    (IESE, Universidad de Navarra)

Abstract

Our purpose in this communication is to present a methodology for forecasting univariate time series. This methodology combines standard forecasting techniques with ``wavelet methodology. The recently developed wavelet theory has proven to be a useful tool in the analysis of some problems in engineering and related fields. However, the potential of this theory for analyzing economic problems has not been fully exploited yet. The communication presents one of its many possible applications in this field. As an example we will apply the methodology to forecast car sales in the Spanish market and compare the results with those given by standard forecasting techniques.

Suggested Citation

  • Miguel A. Ario, "undated". "Forecasting Time Series via Discrete Wavelet Transform," Computing in Economics and Finance 1996 _005, Society for Computational Economics.
    • Handle: RePEc:sce:scecf6:_005
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    File URL: http://www.unige.ch/ce/ce96/ps/arino.eps
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    Cited by:

    1. Laurent Gosse, 2010. "Analysis and short-time extrapolation of stock market indexes through projection onto discrete wavelet subspaces," Post-Print hal-00414210, HAL.

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