Abstract
We consider an extension to a new approach to the linguistic summarization of time series data proposed in our previous papers. We summarize trends identified here with straight segments of a piecewise linear approximation of time series. Then we employ, as a set of features, the duration, dynamics of change and variability, and assume different, human consistent granulations of their values. The problem boils down to a linguistic quantifier driven aggregation of partial trends that is done via the classic Zadeh’s calculus of linguistically quantified propositions but with different t-norms. We show an application to linguistic summarization of time series data on daily quotations of an investment fund over an eight year period.
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References
Batyrshin, I.: On granular derivatives and the solution of a granular initial value problem. International Journal Applied Mathematics and Computer Science 12(3), 403–410 (2002)
Batyrshin, I., Sheremetov, L.: Perception based functions in qualitative forecasting. In: Batyrshin, I., Kacprzyk, J., Sheremetov, L., Zadeh, L.A. (eds.) Perception-based Data Mining and Decision Making in Economics and Finance, Springer, Heidelberg (2006)
Berndt, D.J., Clifford, J.: Finding patterns in time series: a dynamic programming approach. In: Advances in Knowledge Discovery and Data Mining, pp. 229–248. AAAI/MIT Press, Menlo Park, CA (1996)
Chiang, D.-A., Chow, L.R., Wang, Y.-F.: Mining time series data by a fuzzy linguistic summary system. Fuzzy Sets and Systems 112, 419–432 (2000)
Das, G., Lin, K., Mannila, H., Renganathan, G., Smyth, P.: Rule discovery from time series. In: Terano, T., Chen, A.L.P. (eds.) PAKDD 2000. LNCS, vol. 1805, pp. 16–22. Springer, Heidelberg (2000)
Kacprzyk, J., Wilbik, A., Zadrożny, S.: Linguistic summarization of trends: a fuzzy logic based approach. In: Proceedings of the 11th International Conference Information Processing and Management of Uncertainty in Knowledge-based Systems, Paris, France, July 2-7, 2006, pp. 2166–2172 (2006)
Kacprzyk, J., Wilbik, A., Zadrożny, S.: Linguistic summaries of time series via a quantifier based aggregation using the Sugeno integral. In: Kacprzyk, J., Wilbik, A. (eds.) Proceedings of 2006 IEEE World Congress on Computational Intelligence, Vancouver, BC, Canada, July 16-21, 2006, pp. 3610–3616. IEEE Computer Society Press, New York (2006)
Kacprzyk, J., Wilbik, A., Zadrożny, S.: On some types of linguistic summaries of time series. In: Proceedings of the 3rd International IEEE Conference Intelligent Systems, pp. 373–378. IEEE Computer Society Press, London (2006)
Kacprzyk, J., Wilbik, A., Zadrożny, S.: A linguistic quantifier based aggregation for a human consistent summarization of time series. In: Lawry, J., Miranda, E., Bugarin, A., Li, S., Gil, M.A., Grzegorzewski, P., Hryniewicz, O. (eds.) Soft Methods for Integrated Uncertainty Modelling, pp. 186–190. Springer, Heidelberg (2006)
Kacprzyk, J., Wilbik, A., Zadrożny, S.: Capturing the essence of a dynamic behavior of sequences of numerical data using elements of a quasi-natural language. In: Proceedings of the 2006 IEEE International Conference on Systems, Man, and Cybernetics, Taipei, Taiwan, pp. 3365–3370. IEEE Computer Society Press, New York (2006)
Kacprzyk, J., Yager, R.R.: Linguistic summaries of data using fuzzy logic. International Journal of General Systems 30, 33–154 (2001)
Kacprzyk, J., Yager, R.R., Zadrożny, S.: A fuzzy logic based approach to linguistic summaries of databases. International Journal of Applied Mathematics and Computer Science 10, 813–834 (2000)
Kacprzyk, J., Zadrożny, S.: Linguistic database summaries and their protoforms: toward natural language based knowledge discovery tools. Information Sciences 173, 281–304 (2005)
Kacprzyk, J., Zadrożny, S.: Fuzzy linguistic data summaries as a human consistent, user adaptable solution to data mining. In: Gabrys, B., Leiviska, K., Strackeljan, J. (eds.) Do Smart Adaptive Systems Exist? pp. 321–339. Springer, Heidelberg (2005)
Keogh, E., Pazzani, M.: An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback. In: Terano, T., Chen, A.L.P. (eds.) PAKDD 2000. LNCS, vol. 1805, pp. 239–241. Springer, Heidelberg (2000)
Sklansky, J., Gonzalez, V.: Fast polygonal approximation of digitized curves. Pattern Recognition 12(5), 327–331 (1980)
Sripada, S., Reiter, E., Davy, I.: SumTime-Mousam: Configurable Marine Weather Forecast Generator. Expert Update 6(3), 4–10 (2003)
Yager, R.R.: A new approach to the summarization of data. Information Sciences 28, 69–86 (1982)
Yager, R.R.: On ordered weighted averaging aggregation operators in multicriteria decision making. IEEE Transactions on Systems, Man and Cybernetics SMC 18, 183–190 (1988)
Yager, R.R., Kacprzyk, J.: The Ordered Weighted Averaging Operators: Theory and Applications. Kluwer, Boston (1997)
Zadeh, L.A.: A computational approach to fuzzy quantifiers in natural languages. Computers and Mathematics with Applications 9, 149–184 (1983)
Zadeh, L.A.: A prototype-centered approach to adding deduction capabilities to search engines – the concept of a protoform. In: NAFIPS 2002. Proceedings of the Annual Meeting of the North American Fuzzy Information Processing Society, pp. 523–525 (2002)
Zadeh, L.A., Kacprzyk, J. (eds.): Computing with Words in Information/Intelligent Systems: 1. Foundations, 2. Applications. Physica-Verlag, Heidelberg (1999a)
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Kacprzyk, J., Wilbik, A., Zadrożny, S. (2007). Linguistic Summarization of Time Series Under Different Granulation of Describing Features. In: Kryszkiewicz, M., Peters, J.F., Rybinski, H., Skowron, A. (eds) Rough Sets and Intelligent Systems Paradigms. RSEISP 2007. Lecture Notes in Computer Science(), vol 4585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73451-2_25
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DOI: https://doi.org/10.1007/978-3-540-73451-2_25
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