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Evolving Stacked Time Series Predictors with Multiple Window Scales and Sampling Gaps

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Abstract

We apply evolutionary programming to search for the optimal combination of stacked time series predictors with multiple window scales and sampling gaps. In this approach, the evolutionary process is ensured to proceed smoothly towards the optimal solution by using a control strategy based on the similarity level between the genotypes from two successive generations. Our experiments on both sunspots and S&P500 price index predictions demonstrate that this method significantly improves the prediction accuracy compared with the constrained least squared regression.

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Authors and Affiliations

  1. Department of Computer Science, Exeter University, Exeter, EX4 4PT, UK

    Zheng Rong Yang

  2. Department of Physics, Heriot-Watt University, Riccarton, EH14 4AS, UK

    Weiping Lu & Robert G. Harrison

Authors
  1. Zheng Rong Yang
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  2. Weiping Lu
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  3. Robert G. Harrison
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Yang, Z.R., Lu, W. & Harrison, R.G. Evolving Stacked Time Series Predictors with Multiple Window Scales and Sampling Gaps. Neural Processing Letters 13, 203–211 (2001). https://doi.org/10.1023/A:1011392725041

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  • DOI: https://doi.org/10.1023/A:1011392725041

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