Abstract
The paper investigates a generic method of time series classification that is invariant to transformations of time axis. The state-of-art methods widely use Dynamic Time Warping (DTW) with One-Nearest-Neighbor (1NN). We use DTW to transform time axis of each signal in order to decrease the Euclidean distance between signals from the same class. The predictive accuracy of an algorithm that learns from a heterogeneous set of features extracted from signals is analyzed. Feature selection is used to filter out irrelevant predictors and a serial ensemble of decision trees is used for classification. We simulate a dataset for providing a better insight into the algorithm. We also compare our method to DTW+1NN on several publicly available datasets.
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Berndtand, D.J., Clifford, J.: Using dynamic time warping to find patterns in time series. In: Working Notes of the Knowledge Discovery in Databases Workshop, pp. 359–370 (1994)
Ratanamahatana, C.A., Keogh, E.: Everything you know about dynamic time warping is wrong. In: Third Workshop on Mining Temporal and Sequential Data, in conjunction with the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM Press, New York (2004)
Xi, X., Keogh, E., Shelton, C., Wei, L., Ratanamahatana, C.A.: Fast time series classification using numerosity reduction. In: International Conference on Machine Learning (2006)
Keogh, E.: Data mining and machine learning in time series databases (2004)
Hastie, T., Tibshirani, R., Friedman, J.: The elemetns of statistical learning: Data mining, inference, prediction. Springer, Heidelberg (2001)
Eruhimov, V., Martyanov, V., Tuv, E.: Feature class selection for time series classification. In: The Workshop on Time Series Classification, SIGKDD 2007 (submitted, 2007)
Borisov, A., Eruhimov, V., Tuv, E.: Dynamic soft feature selection for tree-based ensembles. In: Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L. (eds.) Feature Extraction, Foundations and Applications, Springer, New York (2006)
Borisov, A., Torkkola, K., Tuv, E.: Best subset feature selection for massive mixed-type problems. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds.) IDEAL 2006. LNCS, vol. 4224, pp. 1048–1056. Springer, Heidelberg (2006)
Keogh, E., Xi, X., Wei, L., Ratanamahatana, C.A.: The ucr time series classification/clustering homepage (2006)
Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoustics, Speech, and Signal Proc. ASSP-26, 43–49 (1978)
Friedman, J.H.: Stochastic gradient boosting. Technical report, Dept. of Statistics, Stanford University (1999)
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Eruhimov, V., Martyanov, V., Tuv, E. (2007). Constructing High Dimensional Feature Space for Time Series Classification. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds) Knowledge Discovery in Databases: PKDD 2007. PKDD 2007. Lecture Notes in Computer Science(), vol 4702. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74976-9_41
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DOI: https://doi.org/10.1007/978-3-540-74976-9_41
Publisher Name: Springer, Berlin, Heidelberg
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