Title Applying a Hybrid Algorithm to the Segmentation of the Spanish Stock Market Index Time Series
Authors Manuel Duran-Rosal, Antonio , de la Paz-Marin, Monica , Antonio Gutierrez, Pedro , Hervas-Martinez, Cesar
External publication No
Means Lect. Notes Comput. Sci.
Scope Proceedings Paper
Nature Científica
JCR Quartile 4
SJR Quartile 2
SJR Impact 0.36900
Publication date 01/01/2015
ISI 000363699900006
Scopus Id 2-s2.0-84937700075
DOI 10.1007/978-3-319-19222-2_6
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.
Keywords Time series segmentation; Hybrid algorithms; Clustering; Spanish stock market index
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