Abstract
This paper presents a local search (LS) method based on the beta distribution for time series segmentation with the purpose of correctly representing extreme values of the underlying variable studied. The LS procedure is combined with an evolutionary algorithm (EA) which segments time series trying to obtain a given number of homogeneous groups of segments. The proposal is tested on a real problem of wave height estimation, where extreme high waves are frequently found. The results show that the LS is able to significantly improve the clustering quality of the solutions obtained by the EA. Moreover, the best segmentation clearly groups extreme waves in a separate cluster and characterizes them according to their centroid.
This work has been subsidized by the project TIN2014-54583-C2-1-R of the Spanish Ministry of Economy and Competitiveness (MINECO), FEDER funds and the P11-TIC-7508 project of the Junta de Andalucía (Spain). Antonio M. Durán-Rosal’s research has been subsidized by the FPU Predoctoral Program of the Spanish Ministry of Education, Culture and Sport (MECD), grant reference FPU14/03039.
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References
Das, G., ip Lin, K., Mannila, H., Renganathan, G., Smyth, P.: Rule Discovery From Time Series. AAAI Press, Menlo Park, pp. 16–22 (1998)
Yang, O., Jia, W., Zhou, P., Meng, X.: A new approach to transforming time series into symbolic sequences. In: Proceedings of the 1st Joint Conference Between the Biomedical Engineering Society and Engineers in Medicine and Biology, p. 974 (1999)
Lin, W., Orgun, M., Williams, G.: An overview of temporal data mining (2002)
Wang, X., Smith, K.A., Hyndman, R.J.: Dimension reduction for clustering time series using global characteristics. In: Sunderam, V.S., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2005. LNCS, vol. 3516, pp. 792–795. Springer, Heidelberg (2005)
Rani, S., Sikka, G.: Recent techniques of clustering of time series data: a survey. Int. J. Comput. Appl. 52(15), 1–9 (2012)
Tseng, V.S., Chen, C.H., Huang, P.C., Hong, T.P.: Cluster-based genetic segmentation of time series with DWT. Pattern Recogn. Lett. 30(13), 1190–1197 (2009)
Nikolaou, A., Gutiérrez, P.A., Durán, A., Dicaire, I., Fernández-Navarro, F., Hervás-Martínez, C.: Detection of early warning signals in paleoclimate data using a genetic time series segmentation algorithm. Clim. Dyn. 44(7), 1919–1933 (2015)
Guralnik, V., Srivastava, J.: Event detection from time series data. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 1999, pp. 3–42. ACM, New York (1999)
Himberg, J., Korpiaho, K., Mannila, H., Tikanmaki, J., Toivonen, H.: Time series segmentation for context recognition in mobile devices. In: Proceedings IEEE International Conference on Data Mining, ICDM 2001, pp. 203–210 (2001)
Chung, F.L., Fu, T.C., Ng, V., Luk, R.W.: An evolutionary approach to pattern-based time series segmentation. IEEE Trans. Evol. Comput. 8(5), 471–489 (2004)
Houck, C.R., Joines, J.A., Kay, M.G.: Comparison of genetic algorithms, random restart and two-opt switching for solving large location-allocation problems. Comput. Oper. Res. 23(6), 587–596 (1996)
Joines, J.A., Kay, M.G., King, R.E., Culbreth, C.T.: A hybrid genetic algorithm for manufacturing cell design. J. Chin. Inst. Ind. Eng. 17(5), 549–564 (2000)
Evans, M., Hastings, N., Peacock, B.: Statistical Distributions. Wiley Series in Probability and Statistics. Wiley, Hoboken (2000)
Durán-Rosal, A.M., de la Paz-Marín, M., Gutiérrez, P.A., Hervás-Martínez, C.: Applying a hybrid algorithm to the segmentation of the Spanish stock market index time series. In: Rojas, I., Joya, G., Catala, A. (eds.) IWANN 2015. LNCS, vol. 9095, pp. 69–79. Springer, Heidelberg (2015)
Calióski, T., Harabasz, J.: A dendrite method for cluster analysis. Commun. Stat. 3(1), 1–27 (1974)
Sato, A.H.: A comprehensive analysis of time series segmentation on Japanese stock prices. Procedia Comput. Sci. 24, 307–314 (2013). 17th Asia Pacific Symposium on Intelligent and Evolutionary Systems, IES 2013
El-Sagheer, R.: Inferences for the generalized logistic distribution based on record statistics. Intell. Inf. Manag. 6, 171–182 (2014)
Menendez, M.: Shannon’s entropy in exponential families: statistical applications. Appl. Math. Lett. 13(1), 37–42 (2000)
Wilks, S.S.: Mathematical Statistics. John Wiley, New York (1963)
National Buoy Data Center. National Oceanic and Atmospheric Administration of the USA (NOAA) (2015). http://www.ndbc.noaa.gov/
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Durán-Rosal, A.M., Dorado-Moreno, M., Gutiérrez, P.A., Hervás-Martínez, C. (2016). On the Use of the Beta Distribution for a Hybrid Time Series Segmentation Algorithm. In: Luaces , O., et al. Advances in Artificial Intelligence. CAEPIA 2016. Lecture Notes in Computer Science(), vol 9868. Springer, Cham. https://doi.org/10.1007/978-3-319-44636-3_39
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