Identifying market behaviours using European Stock Index time series by a hybrid segmentation algorithm

Research areas:

• Methodology - Machine learning

Year:

2017

Type of Publication:

Article

Authors:

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

Journal:

Neural Processing Letters

Volume:

46

Number:

3

Pages:

767-790

Month:

December

ISSN:

1370-4621

BibTex:

@article{ibexDuran2017, 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 = "The discovery of useful patterns embodied in a time series is of fundamental relevance in many real applications. Repetitive structures and common type of segments can also provide very useful information of patterns in financial time series. In this paper, we introduce a time series segmentation and characterization methodology combining a hybrid genetic algorithm and a clustering technique to automatically group common patterns from this kind of financial time series and address the problem of identifying stock market prices trends. This hybrid genetic algorithm includes 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. To do so, we select two stock market index time series: IBEX35 Spanish index (closing prices) and a weighted average (AVG) time series of the IBEX35 (Spanish), BEL20 (Belgian), CAC40 (French) and DAX (German) indexes. These are processed to obtain segments that are mapped into a five dimensional space composed of five statistical measures, with the purpose of grouping them according to their statistical properties. Experimental results show that it is possible to discover homogeneous patterns in both time series.", awards = "JCR(2017): 1.787 Position: 63/132 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", comments = "JCR(2017): 1.787 Position: 63/132 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", issn = "1370-4621", journal = "Neural Processing Letters", month = "December", note = "JCR(2017): 1.787 Position: 63/132 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", number = "3", pages = "767-790", title = "{I}dentifying market behaviours using {E}uropean {S}tock {I}ndex time series by a hybrid segmentation algorithm", url = "doi.org/10.1007/s11063-017-9592-8", volume = "46", year = "2017", }

Note:

JCR(2017): 1.787 Position: 63/132 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Abstract:

The discovery of useful patterns embodied in a time series is of fundamental relevance in many real applications. Repetitive structures and common type of segments can also provide very useful information of patterns in financial time series. In this paper, we introduce a time series segmentation and characterization methodology combining a hybrid genetic algorithm and a clustering technique to automatically group common patterns from this kind of financial time series and address the problem of identifying stock market prices trends. This hybrid genetic algorithm includes 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. To do so, we select two stock market index time series: IBEX35 Spanish index (closing prices) and a weighted average (AVG) time series of the IBEX35 (Spanish), BEL20 (Belgian), CAC40 (French) and DAX (German) indexes. These are processed to obtain segments that are mapped into a five dimensional space composed of five statistical measures, with the purpose of grouping them according to their statistical properties. Experimental results show that it is possible to discover homogeneous patterns in both time series.

Comments:

JCR(2017): 1.787 Position: 63/132 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE