VAR Model Based Clustering Method for Multivariate Time Series Data
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In this study, we develop a clustering method for multivariate time series data. In practical situations, such problems can arise in finance, economics, control theory, and health science. First, we propose to use a simulation based approximation to the test statistic and develop a method to test if two multivariate time series are coming from same VAR process. Then, the testing method is extended to a group of multivariate time series objects. Finally, a new clustering algorithm is developed using the testing method. The proposed algorithm does not use a predetermined number of clusters and finds the best possible clustering from the data. Empirical studies are provided in this paper, and they establish the accuracy of the algorithm. Finally, as a practical example, the algorithm is implemented to identify activities of different persons from a real-life data obtained from single chest-mounted accelerometers worn by different individuals.
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- 3.L. Bao and S. Intille, “Activity recognition from user-annotated acceleration data,” Pervasive Computing, 1–17 (2004).Google Scholar
- 20.J. MacQueen, “Some methods for classification and analysis of multivariate observations,” in: Proc. Fifth Berkeley Sympos. Math. Stat. and Probability, Vol. I: Statistics, University of California Press, Berkeley (1967), pp. 281–297.Google Scholar
- 22.T. Oates, L. Firoiu, and P. Cohen, “Clustering time series with hidden Markov models and dynamic time warping,” in: Proceedings of the IJCAI-99 Workshop on Neural, Symbolic and Reinforcement Learning Methods for Sequence Learning, Stockholm (1999), pp. 17–21.Google Scholar
- 23.T. Santos and R. Kern, “A literature survey of early time series classification and deep learning,” SAMI@ iKNOW (2016).Google Scholar
- 25.P. Smyth et al., “Clustering sequences with hidden Markov models,” Adv. Neur. Inform. Process Syst., 648–654 (1997).Google Scholar
- 29.K. Yang and C. Shahabi, “A PCA-based similarity measure for multivariate time series,” in: Proceedings of the 2nd ACM International Workshop on Multimedia Databases, ACM (2004), pp. 65–74.Google Scholar
- 31.E. Zivot and J. Wang, “Vector autoregressive models for multivariate time series,” Modeling Financial Time Series with S-PLUS, 385–429 (2006).Google Scholar