Applying a Hybrid Algorithm to the Segmentation of the Spanish Stock Market Index Time Series

Áreas de investigación:

• Methodology - Machine learning

Año:

2015

Tipo de publicación:

Artículo en conferencia

Palabras clave:

Time series segmentation, Hybrid algorithms, Clustering, Spanish stock market index

Autores:

• Durán-Rosal, Antonio Manuel
• de la Paz Marín, Mónica
• Gutiérrez, Pedro Antonio
• Hervás-Martínez, César

Volumen:

9095

Título del libro:

13th International Work-Conference on Artificial Neural Networks (IWANN 2015)

Serie:

Lecture Notes in Computer Science

Páginas:

69-79

Organización:

Palma de Mallorca (Spain)

Mes:

10th-12th June

ISBN:

978-3-319-19221-5

BibTex:

@conference{DuranIWANN2015, author = "Antonio Manuel Dur{\'a}n-Rosal and M{\'o}nica de la Paz Mar{\'i}n and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", 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.", booktitle = "13th International Work-Conference on Artificial Neural Networks (IWANN 2015)", doi = "0.1007/978-3-319-19222-2_6", isbn = "978-3-319-19221-5", keywords = "Time series segmentation, Hybrid algorithms, Clustering, Spanish stock market index", month = "10th-12th June", organization = "Palma de Mallorca (Spain)", pages = "69--79", publisher = "Springer International Publishing", series = "Lecture Notes in Computer Science", title = "{A}pplying a {H}ybrid {A}lgorithm to the {S}egmentation of the {S}panish {S}tock {M}arket {I}ndex {T}ime {S}eries", url = "http://dx.doi.org/10.1007/978-3-319-19222-2_6", volume = "9095", year = "2015", }

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.