Title Time Series Representation by a Novel Hybrid Segmentation Algorithm
Authors DURAN ROSAL, ANTONIO MANUEL, Antonio Gutierrez-Pena, Pedro , MARTÍNEZ ESTUDILLO, FRANCISCO JOSÉ, Hervas-Martinez, Cesar
External publication No
Means Lect. Notes Comput. Sci.
Scope Proceedings Paper
Nature Científica
JCR Quartile 4
SJR Quartile 2
SJR Impact 0.339
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964070282&doi=10.1007%2f978-3-319-32034-2_14&partnerID=40&md5=d82e66a7d2be72baa4c339c97096bb72
Publication date 01/01/2016
ISI 000389499600014
Scopus Id 2-s2.0-84964070282
DOI 10.1007/978-3-319-32034-2_14
Abstract Time series representation can be approached by segmentation genetic algorithms (GAs) with the purpose of automatically finding segments approximating the time series with the lowest possible error. Although this is an interesting data mining field, obtaining the optimal segmentation of time series in different scopes is a very challenging task. In this way, very accurate algorithms are needed. On the other hand, it is well-known that GAs are relatively poor when finding the precise optimum solution in the region where they converge. Thus, this paper 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 two well-known algorithms: Bottom-Up and Top-Down. A real-world time series in the Spanish Stock Market field (IBEX35) and a synthetic database (Donoho-Johnstone) used in other researches were used to test the proposed methodology.
Keywords Time series segmentation; Hybrid algorithms; Time series representation; Spanish stock market index; Synthetic database
Universidad Loyola members

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