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| Time series prediction is a typical and significant task in data mining, which has been widely studied recently. This paper proposes two novel methods for Time Series Prediction based on GEP (Gene Expression Programming). The main contributions are as follows: (1) GEP-Sliding Window Prediction Method (GEP-SWPM) to mine the relationship between future and history data directly. (2) GEP-Differential Equation Prediction Method (GEP-DEPM) to mine ordinary differential equation from training data, and predicts future trends based on specified initial conditions. (3) A brand new data preprocessing method, called Differential by Microscope Interpolation (DMI) that boosts the effectivity of our methods. (4) A new simple and effective GEP-constants generation method called Meta-Constants (MC) is also proposed. Extensive experiments on real data sets for sun spot prediction show that the performance of the new method is 10-200 times higher than existing algorithms.
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| Keywords:GEP (Gene Expression Programming), Data Mining, Time Series Prediction, Sun Spot Prediction, Differential Equation |
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