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.
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References
Fu, T.C.: A review on time series data mining. Eng. Appl. Artif. Intell. 24(1), 164–181 (2011)
Cheong, S.A., Fornia, R.P., Lee, G.H.T., Kok, J.L., Yim, W.S., Xu, D.Y., Zhang, Y.: The japanese economy in crises: A time series segmentation study. Economics: The Open-Access. Open-Assessment E-Journal 6(2012-5) (2012)
Wong, J., Lian, H., Cheong, S.: Detecting macroeconomic phases in the dow jones industrial average time series. Physica A 388(21), 4635–4645 (2009)
Edwards, R., Magee, J.: Technical analysis of stock trends. Magee, Springfield/Mass (2013)
Pagan, A.R., Sossounov, K.A.: A simple framework for analysing bull and bear markets. Journal of Applied Econometrics 18(1), 23–46 (2003)
Basseville, M., Nikiforov, I.V.: Detection of Abrupt Changes: Theory and Application. Prentice-Hall, Inc., Upper Saddle River (1993)
Keogh, E., Chu, S., Hart, D., Pazzani, M.: An online algorithm for segmenting time series. In: Proceedings of the IEEE International Conference on Data Mining, ICDM 2001, pp. 289–296 (2001)
Appel, U., Brandt, A.V.: Adaptive sequential segmentation of piecewise stationary time series. Information Sciences 29(1), 27–56 (1983)
Basseville, M., Benveniste, A.: Sequential segmentation of nonstationary digital signals using spectral analysis. Information Sciences 29(1), 57–73 (1983)
Hawkins, D.: A test for a change point in a parametric model based on a maximal Wald-type statistic. Sankhya 49, 368–376 (1987)
Das, G., Lin, K.I., Mannila, H., Renganathan, G., Smyth, P.: Rule discovery from time series, pp. 16–22. AAAI Press (1998)
Ou-Yang, K., Jia, W., Zhou, P., Meng, X.: A new approach to transforming time series into symbolic sequences. In: 21st Annual Conference on Engineering in Medicine and Biology and the 1999 Annual Fall Meetring of the Biomedical Engineering Society, Proceedings of the First Joint BMES/EMBS Conference, vol. 2, p. 974 (October 1999)
Bernaola-Galván, P., Román-Roldán, R., Oliver, J.L.: Compositional segmentation and long-range fractal correlations in dna sequences. Phys. Rev. E 53, 5181–5189 (1996)
Sato, A.H.: A comprehensive analysis of time series segmentation on japanese stock prices. Procedia Computer Science 24, 307–314 (2013); 17th Asia Pacific Symposium on Intelligent and Evolutionary Systems, IES 2013
Xu, R., Wunsch, D.: Clustering. IEEE Press Series on Computational Intelligence. Wiley (2008)
Cohen, S., Intrator, N.: Global optimization of rbf networks (2000)
Chung, F.L., Fu, T.C., Ng, V., Luk, R.W.: An evolutionary approach to pattern-based time series segmentation. IEEE Transactions on Evolutionary Computation 8(5), 471–489 (2004)
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Cruz-Ramírez, M., de la Paz-Marín, M., Pérez-Ortiz, M., Hervás-Martínez, C. (2014). Time Series Segmentation and Statistical Characterisation of the Spanish Stock Market Ibex-35 Index. In: Polycarpou, M., de Carvalho, A.C.P.L.F., Pan, JS., Woźniak, M., Quintian, H., Corchado, E. (eds) Hybrid Artificial Intelligence Systems. HAIS 2014. Lecture Notes in Computer Science(), vol 8480. Springer, Cham. https://doi.org/10.1007/978-3-319-07617-1_7
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DOI: https://doi.org/10.1007/978-3-319-07617-1_7
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07616-4
Online ISBN: 978-3-319-07617-1
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