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Time Series Representation by a Novel Hybrid Segmentation Algorithm

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Hybrid Artificial Intelligent Systems (HAIS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9648))

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Abstract

Time series representation can be approached by segmentation genetic algorithms (GAs) with the purpose of automatically finding segments approximating the time series with the lowest possible error. Although this is an interesting data mining field, obtaining the optimal segmentation of time series in different scopes is a very challenging task. In this way, very accurate algorithms are needed. On the other hand, it is well-known that GAs are relatively poor when finding the precise optimum solution in the region where they converge. Thus, this paper presents a hybrid GA algorithm including a local search method, aimed to improve the quality of the final solution. The local search algorithm is based on two well-known algorithms: Bottom-Up and Top-Down. A real-world time series in the Spanish Stock Market field (IBEX35) and a synthetic database (Donoho-Johnstone) used in other researches were used to test the proposed methodology.

This work has been subsidized by the project TIN2014-54583-C2-1-R of the Spanish Ministerial Commission of Science and Technology (MICYT), 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 (Spanish Ministry of Education and Science), grant reference FPU14/03039.

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Notes

  1. 1.

    The corresponding values can be downloaded at https://es.finance.yahoo.com/.

  2. 2.

    The time series can be downloaded at https://sites.google.com/site/icdmmdl/.

References

  1. 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)

    Article  Google Scholar 

  2. Das, G., Lin, K., Mannila, H., Renganathan, G., Smyth, P.: Rule discovery from time series, pp. 16–22. AAAI Press (1998)

    Google Scholar 

  3. Nikolaou, A., Gutiérrez, P., Durán, A., Dicaire, I., Fernández-Navarro, F., Hervás-Martínez, C.: Detection of early warning signals in paleoclimate data using agenetic time series segmentation algorithm. Clim. Dyn. 44(7), 1919–1933 (2014)

    Google Scholar 

  4. Fuchs, E., Gruber, T., Nitschke, J., Sick, B.: On-line motif detection in time series with swiftmotif. Pattern Recogn. 42(11), 3015–3031 (2009)

    Article  MATH  Google Scholar 

  5. Oliver, J., Forbes, C.: Bayesian approaches to segmenting a simple time series. Technical Report 14/97, Monash University, Department of Econometrics and Business Statistics (1997)

    Google Scholar 

  6. Oliver, J.J., Baxter, R.A., Wallace, C.S.: Minimum message length segmentation. In: Wu, X., Kotagiri, R., Korb, K. (eds.) PAKDD 1998. LNCS, vol. 1394, pp. 222–233. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  7. Keogh, E.J., Chu, S., Hart, D., Pazzani, M.: Segmenting Time Series: A Survey and Novel Approach. In: Last, M., Kandel, A., Bunke, H. (eds.) Data Mining in Time Series Databases. Series in Machine Perception and Artificial Intelligence, vol. 57, pp. 1–22. World Scientific Publishing Company (2004)

    Google Scholar 

  8. Houck, C.R., Joines, J.A., Kay, M.G., Wilson, J.R.: Empirical investigation of the benefits of partial lamarckianism. Evol. Comput. 5(1), 31–60 (1997)

    Article  Google Scholar 

  9. Kolen, A., Pesch, E.: Genetic local search in combinatorial optimization. Discrete Appl. Math. 48(3), 273–284 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  10. Joines, J.A., Kay, M.G.: Utilizing hybrid genetic algorithms. In: Sarker, R., Mohammadian, M., Yao, X. (eds.) Evolutionary Optimization. International Series in Operations Research & Management Science, vol. 48, pp. 199–228. Springer, US (2002)

    Google Scholar 

  11. van Ulder, N.L.J., Aarts, E.H.L., Bandelt, H.J., Laarhoven, P.J.M., Pesch, E.: Genetic local search algorithms for the travelling salesman problem. In: Schwefel, H.-P., Mánner, R. (eds.) PPSN I. LNCS, vol. 496, pp. 109–116. Springer, Heidelberg (1991)

    Chapter  Google Scholar 

  12. Donoho, D.L., Johnstone, J.M.: Ideal spatial adaptation by wavelet shrinkage. Biometrika 81(3), 425–455 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  13. Donoho, D.L., Johnstone, I.M., Kerkyacharian, G., Picard, D.: Wavelet shrinkage: asymptopia? J. Royal Stat. Soc. Ser. B (Methodol.) 57(2), 301–369 (1995)

    MathSciNet  MATH  Google Scholar 

  14. Donoho, D.L., Johnstone, I.M.: Adapting to unknown smoothness via wavelet shrinkage. J. Am. Stat. Assoc. 90, 1200–1224 (1995)

    Article  MathSciNet  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science and Numerical Analysis, University of Córdoba, Rabanales Campus, Albert Einstein Building, 14071, Córdoba, Spain

    Antonio Manuel Durán-Rosal, Pedro Antonio Gutiérrez-Peña & César Hervás-Martínez

  2. Department of Quantitative Methods, Loyola Andalucía University, Escritor Castilla Aguayo 4, 14004, Córdoba, Spain

    Francisco José Martínez-Estudillo

Authors
  1. Antonio Manuel Durán-Rosal
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  2. Pedro Antonio Gutiérrez-Peña
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  3. Francisco José Martínez-Estudillo
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  4. César Hervás-Martínez
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Corresponding author

Correspondence to Antonio Manuel Durán-Rosal .

Editor information

Editors and Affiliations

  1. Universidad Pablo de Olavide, Sevilla, Spain

    Francisco Martínez-Álvarez

  2. Universidad Pablo de Olavide, Sevilla, Spain

    Alicia Troncoso

  3. University of Salamanca, Salamanca, Spain

    Héctor Quintián

  4. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Durán-Rosal, A.M., Gutiérrez-Peña, P.A., Martínez-Estudillo, F.J., Hervás-Martínez, C. (2016). Time Series Representation by a Novel Hybrid Segmentation Algorithm. In: Martínez-Álvarez, F., Troncoso, A., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2016. Lecture Notes in Computer Science(), vol 9648. Springer, Cham. https://doi.org/10.1007/978-3-319-32034-2_14

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  • DOI: https://doi.org/10.1007/978-3-319-32034-2_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32033-5

  • Online ISBN: 978-3-319-32034-2

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