Forecasting Time Series via Discrete Wavelet Transform

Miguel A. Ario ()
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Miguel A. Ario: IESE, Universidad de Navarra

Computing in Economics and Finance 1996 from Society for Computational Economics

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.

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