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

摘要

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.