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

Research areas:

• Methodology - Machine learning

Year:

2014

Type of Publication:

In Proceedings

Keywords:

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

Authors:

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

Volume:

8480

Book title:

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

Series:

Lecture Notes in Computer Science

Pages:

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.