Time series segmentation and statistical characterisation of the Spanish stock market Ibex-35 index

Áreas de investigación:

• Methodology - Machine learning

Año:

2014

Tipo de publicación:

Artículo en conferencia

Palabras clave:

Clustering,Ibex-35 index,segmentation,stock market,time series

Autores:

• Cruz-Ramírez, Manuel
• de la Paz Marín, Mónica
• Pérez-Ortiz, María
• Hervás-Martínez, César

Volumen:

8480

Título del libro:

Proceedings of the 9th International Conference on Hybrid Artificial Intelligence Systems (HAIS2014)

Serie:

Lecture Notes in Computer Science

Páginas:

74-85

ISBN:

978-3-319-07616-4

ISSN:

0302-9743

BibTex:

@conference{TSSIbex35HAIS, author = "Manuel Cruz-Ram{\'i}rez and M{\'o}nica de la Paz Mar{\'i}n and Mar{\'i}a P{\'e}rez-Ortiz and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "The discovery of characteristic time series patterns is of fundamental importance in financial applications. Repetitive structures and common type of segments can provide very useful information of patterns in financial time series. In this paper, we introduce a time series segmentation and characterisation methodology combining a maximal likelihood optimisation procedure and a clustering technique to automatically segment common patterns from financial time series and address the problem of stock market prices trends. To do so, the obtained segments are transformed into a five-dimensional space composed of five typical statistical measures in order to group them according to their statistical properties. The experimental results show that it is possible to exploit the behaviour of the stock market Ibex-35 Spanish index (closing prices) to detect homogeneous segments of the time series.", booktitle = "Proceedings of the 9th International Conference on Hybrid Artificial Intelligence Systems (HAIS2014)", doi = "10.1007/978-3-319-07617-1_7", isbn = "978-3-319-07616-4", issn = "0302-9743", keywords = "Clustering,Ibex-35 index,segmentation,stock market,time series", pages = "74--85", publisher = "Springer", series = "Lecture Notes in Computer Science", title = "{T}ime series segmentation and statistical characterisation of the {S}panish stock market {I}bex-35 index", url = "http://dx.doi.org/10.1007/978-3-319-07617-1_7", volume = "8480", year = "2014", }

Abstract:

The discovery of characteristic time series patterns is of fundamental importance in financial applications. Repetitive structures and common type of segments can provide very useful information of patterns in financial time series. In this paper, we introduce a time series segmentation and characterisation methodology combining a maximal likelihood optimisation procedure and a clustering technique to automatically segment common patterns from financial time series and address the problem of stock market prices trends. To do so, the obtained segments are transformed into a five-dimensional space composed of five typical statistical measures in order to group them according to their statistical properties. The experimental results show that it is possible to exploit the behaviour of the stock market Ibex-35 Spanish index (closing prices) to detect homogeneous segments of the time series.