Abstract
In this paper we propose a solution to the problem of distinguishing between causal and acausal temporal sets of rules. The method, called the Temporal Investigation Method for Enregistered Record Sequences (TIMERS), is explained and introduced formally. The input to TIMERS consists of a sequence of records, where each record is observed at regular intervals. Sets of rules are generated from the input data using different window sizes and directions of time. The set of rules may describe an instantaneous relationship, where the decision attribute depends on condition attributes seen at the same time instant. We investigate the temporal characteristics of the system by changing the direction of time when generating temporal rules to see whether a set of rules is causal or acausal. The results are used to declare a verdict as to the nature of the system: instantaneous, causal, or acausal.
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References
Hoeppner, F., Discovery of Temporal Patterns: Learning Rules about the Qualitative Behaviour of Time Series, Principles of Data Mining and Knowledge Discovery (PKDD’2001), 2001.
Karimi, K., and Hamilton, H.J., TimeSleuth: A Tool for Discovering Causal and Temporal Rules, 14th IEEE International Conference On Tools with Artificial Intelligence (ICTAI’2002), Washington DC, USA, November 2002, pp. 375–380.
Karimi K., and Hamilton H.J., Learning With C4.5 in a Situation Calculus Domain, The Twentieth SGES International Conference on Knowledge Based Systems and Applied Artificial Intelligence (ES2000), Cambridge, UK, December 2000, pp. 73–85.
Karimi K., and Hamilton H.J., Discovering Temporal Rules from Temporally Ordered Data, The Third International Conference on Intelligent Data Engineering and Automated Learning (IDEAL’2002), Manchester, UK, August 2002, pp. 334–338.
Kennett, R.J., Korb, K.B., and Nicholson, A.E., Seabreeze Prediction Using Bayesian Networks: A Case Study, Proc. Fifth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD’01). Hong Kong, April 2001.
Krener, A. J. Acausal Realization Theory, Part I; Linear Deterministic Systems. SIAM Journal on Control and Optimization. 1987. Vol 25, No 3, pp. 499–525.
Pearl, J., Causality: Models, Reasoning, and Inference, Cambridge University Press. 2000.
Scheines, R., Spirtes, P., Glymour, C. and Meek, C., Tetrad II: Tools for Causal Modeling, Lawrence Erlbaum Associates, Hillsdale, NJ, 1994.
Schwarz, R. J. and Friedland B., Linear Systems. McGraw-Hill, New York. 1965.
http://typhoon.bae.lsu.edu/datatabl/current/sugcurrh.html. Content varies.
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© 2003 Springer-Verlag Berlin Heidelberg
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Karimi, K., Hamilton, H.J. (2003). Distinguishing Causal and Acausal Temporal Relations. In: Whang, KY., Jeon, J., Shim, K., Srivastava, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2003. Lecture Notes in Computer Science(), vol 2637. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36175-8_23
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DOI: https://doi.org/10.1007/3-540-36175-8_23
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