Learning Time Series Patterns by Genetic Programming

Xie, F., Song, A. and Ciesielski, V.

Finding patterns such as increasing or decreasing trends, abrupt changes and periodically repeating sequences is a necessary task in many real world situations. We have shown how genetic programming can be used to detect increasingly complex patterns in time series data. Most classification methods require a hand-crafted feature extraction preprocessing step to accurately perform such tasks. In contrast, the evolved programs operate on the raw time series data. On the more difficult problems the evolved classifiers outperform the OneR, J48, Naive Bayes, IB1 and Adaboost classifiers by a large margin. Furthermore this method can handle noisy data. Our results suggest that the genetic programming approach could be used for detecting a wide range of patterns in time series data without extra processing or feature extraction.

Cite as: Xie, F., Song, A. and Ciesielski, V. (2012). Learning Time Series Patterns by Genetic Programming. In Proc. Australasian Computer Science Conference (ACSC 2012) Melbourne, Australia. CRPIT, 122. Reynolds, M. and Thomas, B, Eds., ACS. 57-62

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