IDA 2013: Advances in Intelligent Data Analysis XII pp 198-209 | Cite as
Learning Multiple Temporal Matching for Time Series Classification
Abstract
In real applications, time series are generally of complex structure, exhibiting different global behaviors within classes. To discriminate such challenging time series, we propose a multiple temporal matching approach that reveals the commonly shared features within classes, and the most differential ones across classes. For this, we rely on a new framework based on the variance/covariance criterion to strengthen or weaken matched observations according to the induced variability within and between classes. The experiments performed on real and synthetic datasets demonstrate the ability of the multiple temporal matching approach to capture fine-grained distinctions between time series.
Keywords
Time Series Synthetic Dataset Dynamic Time Warping Discriminative Feature Multivariate Time SeriesPreview
Unable to display preview. Download preview PDF.
References
- 1.Kruskall, J., Liberman, M.: The symmetric time warping algorithm: From continuous to discrete. In: Time Warps, String Edits and Macromolecules. Addison-Wesley (1983)Google Scholar
- 2.Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing 26(1), 43–49 (1978)CrossRefMATHGoogle Scholar
- 3.Yu, D., Yu, X., Hu, Q., Liu, J., Wu, A.: Dynamic time warping constraint learning for large margin nearest neighbor classification. Information Sciences 181, 2787–2796 (2011)CrossRefGoogle Scholar
- 4.Jeong, Y., Jeong, M., Omitaomu, O.: Weighted dynamic time warping for time series classification. Pattern Recognition 44, 2231–2240 (2011)CrossRefGoogle Scholar
- 5.Douzal-Chouakria, A., Amblard, C.: Classification trees for time series. Pattern Recognition 45(3), 1076–1091 (2012)CrossRefGoogle Scholar
- 6.Ye, L., Keogh, E.: Time series shapelets: A new primitive for data mining. Data Min. Knowl. Disc. 22, 149–182 (2011)MathSciNetCrossRefMATHGoogle Scholar
- 7.Yamada, Y., Suzuki, E., Yokoi, H., Takabayashi, K.: Decision-tree induction from time-series data based on standard-example split test. In: Proceedings of the 20th International Conference on Machine Learning, pp. 840–847. Morgan Kaufmann (2003)Google Scholar
- 8.Peter, S., Höppner, F., Berthold, M.: Pattern graphs: A knowledge-based tool for multivariate temporal pattern retrieval. In: IEEE Conf. Intelligent Systems (2012)Google Scholar
- 9.Peter, S., Höppner, F., Berthold, M.R.: Learning pattern graphs for multivariate temporal pattern retrieval. In: Hollmén, J., Klawonn, F., Tucker, A. (eds.) IDA 2012. LNCS, vol. 7619, pp. 264–275. Springer, Heidelberg (2012)CrossRefGoogle Scholar
- 10.Fisher, R.: The use of multiple measures in taxonomic problems. Annals of Eugenics 7, 179–188 (1936)CrossRefGoogle Scholar
- 11.Hebrail, G., Hugueney, B., Lechevallier, Y., Rossi, F.: Exploratory analysis of functional data via clustering and optimal segmentation. Neurocomputing 73, 1125–1141 (2010)CrossRefGoogle Scholar
- 12.Asuncion, A., Newman, D.: uci Machine learning repository (2007)Google Scholar