Abstract
Time-series segmentation can be approached by combining a clustering technique and genetic algorithm (GA) with the purpose of automatically finding segments and patterns of a time series. This is an interesting data mining field, but its application to the optimal segmentation of financial time series is a very challenging task, so accurate algorithms are needed. In this sense, GAs are relatively poor at finding the precise optimum solution in the region where the algorithm converges. Thus, this work presents a hybrid GA algorithm including a local search method, aimed to improve the quality of the final solution. The local search algorithm is based on maximizing a likelihood ratio, assuming normality for the series and the subseries in which the original one is segmented. A real-world time series in the Spanish Stock Market field was used to test this methodology.
This work has been partially subsidised by the TIN2011-22794 project of the Spanish Ministry of Economy and Competitiveness (MINECO), FEDER funds and the P2011-TIC-7508 project of the “Junta de Andalucía” (Spain).
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Durán-Rosal, A.M., de la Paz-Marín, M., Gutiérrez, P.A., Hervás-Martínez, C. (2015). Applying a Hybrid Algorithm to the Segmentation of the Spanish Stock Market Index Time Series. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2015. Lecture Notes in Computer Science(), vol 9095. Springer, Cham. https://doi.org/10.1007/978-3-319-19222-2_6
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DOI: https://doi.org/10.1007/978-3-319-19222-2_6
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