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Mining Causal Relationships in Multidimensional Time Series

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Smart Information and Knowledge Management

Part of the book series: Studies in Computational Intelligence ((SCI,volume 260))

Abstract

Time series are ubiquitous in all domains of human endeavor. They are generated, stored, and manipulated during any kind of activity. The goal of this chapter is to introduce a novel approach to mine multidimensional time-series data for causal relationships. The main feature of the proposed system is supporting discovery of causal relations based on automatically discovered recurring patterns in the input time series. This is achieved by integrating a variety of data mining techniques.

The main insight of the proposed system is that causal relations can be found more easily and robustly by analyzing meaningful events in the time series rather than by analyzing the time series numerical values directly. The RSST (Robust Singular Spectrum Transform) algorithm is used to find interesting points in every time series that is further analyzed by a constrained motif discovery algorithm (if needed) to learn basic events of the time series. The Granger-causality test is extended and applied to the multidimensional time-series describing the occurrences of these basic events rather than to the raw time-series data.

The combined algorithm is evaluated using both synthetic and real world data. The real world application is to mine records of activities during a human-robot interaction experiment in which a human subject is guiding a robot to navigate using free hand gesture. The results show that the combined system can provide causality graphs representing the underlying relations between the human’s actions and robot behavior that cannot be recovered using standard causal graph learning procedures.

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References

  1. Wang, X., Ye, L., Keogh, E., Shelton, C.: Annotating historical archives of images. In: JCDL 2008: Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries, pp. 341–350. ACM, New York (2008)

    Chapter  Google Scholar 

  2. Inokuchi, A., Washio, T.: Feasibility of graph sequence mining based on admissibility constraints. In: Thid International Workshop on Data Mining and Statistical Science, pp. 1–4 (2008)

    Google Scholar 

  3. 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)

    Google Scholar 

  4. Kulic, D., Takano, W., Nakamura, Y.: Incremental on-line hierarchical clustering of whole body motion patterns. In: RO-MAN 2007 (2007)

    Google Scholar 

  5. Keogh, E., Lin, J., Fu, A.: Hot sax: efficiently finding the most unusual time series subsequence. In: Fifth IEEE International Conference on Data Mining, November 2005, pp. 226–233 (2005)

    Google Scholar 

  6. Mohammad, Y., Nishida, T.: Constrained motif discovery. In: International Workshop on Data Mining and Statistical Science (DMSS 2008), September 2008, pp. 16–19 (2008)

    Google Scholar 

  7. Aristotle: Metaphysics Book V Part 1

    Google Scholar 

  8. Glymour, C.: Learning, prediction and causalbayesnets. TRENDS in Cognitive Sciences 7(1), 43–48 (2003)

    Article  MathSciNet  Google Scholar 

  9. Menzies, P., Price, H.: Causation as a secondary quality. British Journal for the Philosophy of Science 4, 187–203 (1993)

    Article  Google Scholar 

  10. Pearl, J.: Causality: Models, Reasoning, and Inferenc. Cambridge University Press, Cambridge (2000)

    Google Scholar 

  11. Han, J., Fu, Y., Wang, W., Chiang, J., Gong, W., Koperski, K., Li, D., Lu, Y., Rajan, A., Stefanovic, N., Xia, B., Zaiane, O.R.: Dbminer: A system for mining knowledge in large relational databases. In: Proc. 1996 Int’l Conf. on Data Mining and Knowledge Discovery (KDD 1996), pp. 250–255. AAAI Press, Menlo Park (1996)

    Google Scholar 

  12. Das, K., Lin, I., Mannila, H., Renganathan, G., Smyth, P.: Rule discovery from time series. In: The 4th International Conference of Knowledge Discovery and Data Mining, pp. 16–22. AAAI Press, Menlo Park (1998)

    Google Scholar 

  13. Hipp, J., Güntzer, U., Nakhaeizadeh, G.: Algorithms for association rule mining — a general survey and comparison. SIGKDD Explor. Newsl. 2(1), 58–64 (2000)

    Article  Google Scholar 

  14. Lee, A.J., chuen Lin, W., sheng Wang, C.: Mining association rules with multi-dimensional constraints. Journal of Systems and Software 79, 79–92 (2006)

    Article  Google Scholar 

  15. Sarker, B.K., Hirata, T., Uehara, K., Bhavsar, V.C.: Mining Association Rules from Multi-stream Time Series Data on Multiprocessor Systems. In: Pan, Y., Chen, D.-x., Guo, M., Cao, J., Dongarra, J. (eds.) ISPA 2005. LNCS, vol. 3758, pp. 662–667. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  16. Hoover, K.: The logic of causal inference. Economics and Philosophy 6, 207–234 (1990)

    Article  Google Scholar 

  17. Tian, J., Pearl, J.: Causal discovery from changes. In: Proceedings of UAI 2001, pp. 512–521. Morgan Kaufmann, San Francisco (2001)

    Google Scholar 

  18. Gelper, S., Croux, C.: Multivariate out-of-sample tests for granger causality. Comput. Stat. Data Anal. 51(7), 3319–3329 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  19. Ding, M., Chen, Y., Bressler, S.: Granger causality: Basic theory and application to neuroscience. Wiley, Chichester (2006)

    Google Scholar 

  20. Karimi, K., Hamilton, H.J.: Distinguishing Causal and Acausal Temporal Relations. In: Advances in Knowledge Discovery and Data Mining., p. 569. Springer, Heidelberg (2003)

    Google Scholar 

  21. Spirtes, P., Glymour, C.N., Scheines, R.: Causation, prediction, and search. MIT Press, Cambridge (2001)

    MATH  Google Scholar 

  22. Basseville, M., Kikiforov, I.: Detection of Abrupt Changes. Printice Hall, Englewood Cliffs (1993)

    Google Scholar 

  23. Kadambe, S., Boudreaux-Bartels, G.: Application of the wavelet transform for pitch detection of speech signals. IEEE Transactions on Information Theory 38(2), 917–924 (1992)

    Article  Google Scholar 

  24. Hirano, S., Tsumoto, S.: Mining similar temporal patterns in long time-series data and its application to medicine. In: ICDM 2002: Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM 2002), p. 219. IEEE Computer Society, Washington (2002)

    Chapter  Google Scholar 

  25. Gombay, E.: Change detection in autoregressive time series. J. Multivar. Anal. 99(3), 451–464 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  26. Ide, T., Inoue, K.: Knowledge discovery from heterogeneous dynamic systems using change-point correlations. In: Proc. SIAM Intl. Conf. Data Mining (2005)

    Google Scholar 

  27. Zha, H., Simon, H.D.: On updating problems in latent semantic indexing. SIAM Journal on Scientific Computing 21(2), 782–791 (1999)

    Article  MATH  MathSciNet  Google Scholar 

  28. Ide, T., Tsuda, K.: Change-point detection using krylov subspace learning. In: Proceedings of the SIAM Internations Conference on Data Mining (2007)

    Google Scholar 

  29. Mohammad, Y., Nishida, T.: Robust singular spectrum transform. In: IEA/AIE, pp. 123–132 (2009)

    Google Scholar 

  30. Chiu, B., Keogh, E., Lonardi, S.: Probabilistic discovery of time series motifs. In: KDD 2003: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 493–498. ACM, New York (2003)

    Chapter  Google Scholar 

  31. Oates, T.: Peruse: An unsupervised algorithm for finding recurring patterns in time series. In: International Conference on Data Mining, pp. 330–337 (2002)

    Google Scholar 

  32. Jensen, K.L., Styczynxki, M.P., Rigoutsos, I., Stephanopoulos, G.N.: A generic motif discovery algorithm for sequenctial data. BioInformatics 22(1), 21–28 (2006)

    Article  Google Scholar 

  33. Minnen, D., Starner, T., Essa, I., Isbell, C.: Improving activity discovery with automatic neighborhood estimation. In: Int. Joint Conf. on Artificial Intelligence, pp. 6–12 (2007)

    Google Scholar 

  34. Catalano, J., Armstrong, T., Oates, T.: Discovering patterns in real-valued time series. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS (LNAI), vol. 4213, pp. 462–469. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  35. Freedman, D., Humphreys, P.: Are there algorithms that discover causal structure. Synthese 121(1-2), 29–54 (2004)

    Google Scholar 

  36. Mohammad, Y., Nishida, T.: Human adaptation to a miniature robot: Precursors of mutual adaptation. In: The 17th IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN 2008, pp. 124–129 (2008)

    Google Scholar 

  37. Ohmura, R., Naya, F., Noma, H., Kogure, K.: a bluetooth-based wearable sensing device for nursing activity recognition. In: 2006 1st International Symposium on Wireless Pervasive Computing, January 2006, pp. 1686–1693 (2006)

    Google Scholar 

  38. PhaseSpace Inc., http://www.phasespace.com

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Author information

Authors and Affiliations

  1. Graduate School of Informatics, Kyoto University,  

    Yasser Mohammad & Toyoaki Nishida

Authors
  1. Yasser Mohammad
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  2. Toyoaki Nishida
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Editors and Affiliations

  1. Faculty of Management and Economics, Gdansk University of Technology, Str.Narutowicza 11/12, 80-233, Gdansk, Poland

    Edward Szczerbicki

  2. Institute of Informatics, Wroclaw University of Technology, Str.Wyb.Wyspianskiego 27, 50-370, Wroclaw, Poland

    Ngoc Thanh Nguyen

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Mohammad, Y., Nishida, T. (2010). Mining Causal Relationships in Multidimensional Time Series. In: Szczerbicki, E., Nguyen, N.T. (eds) Smart Information and Knowledge Management. Studies in Computational Intelligence, vol 260. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04584-4_14

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  • DOI: https://doi.org/10.1007/978-3-642-04584-4_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04583-7

  • Online ISBN: 978-3-642-04584-4

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