BazEkon - Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie

BazEkon home page

Meny główne

Kacprzyk Janusz, Wilbik Anna, Zadrożny Sławomir
Linguistic Summaries of Time Series as Tools for the Analysis of Trends
Roczniki Kolegium Analiz Ekonomicznych / Szkoła Główna Handlowa, 2007, nr 17, s. 167-186, rys., bibliogr. 30 poz.30
Tytuł własny numeru
Discovering patterns in economic data
Słowa kluczowe
System informacyjny, Fundusze inwestycyjne, Data Mining, Szeregi czasowe
Information system, Investment funds, Data Mining, Time-series
We propose an approach to a linguistic summarization of numeric time series data. Basically, the proposed summaries of time series refer to the linguistic summaries of what happens - in the sens e of dynamics - to trends identified here with straight line segments of a piece-wise linear approximation of me series. As main attributes which are used for the summarization we use the duration, dynamics (slope) and variability of trends. The derivation of a linguistic summary of a time series is then related to a linguistic quantifier driven aggregation of trends. For this purpose we employ the classic Zadeh's calculus of linguistically quantified propositions in its basic form. We show an application to the absolute performance type analysis of time series data on daily quotations of an investment fund over an eight year period, and present some interesting linguistic summaries obtained. (original abstract)
Dostępne w
Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie
Biblioteka SGH im. Profesora Andrzeja Grodka
Biblioteka Główna Uniwersytetu Ekonomicznego w Katowicach
Biblioteka Główna Uniwersytetu Ekonomicznego w Poznaniu
Biblioteka Główna Uniwersytetu Ekonomicznego we Wrocławiu
  1. Batyrshin, On granular derivatives and the solution of a granular initial value problem, International Journal Applied Mathematics and Computer Science 12 (3) (2002) 403-410.
  2. I. Batyrshin, L. Sheremetov, Perception based functions in qualitative forecasting, in: I. Batyrshin, J. Kacprzyk, L. Sheremetov, L. A. Zadeh (Eds.), Perception-based Data Mining and Decision Making in Economics and Finance, Springer-Verlag, Berlin and Heidelberg, 2006.
  3. D.J. Bemdt, J. Clifford (1996). Finding patterns in time series: a dynamic programming approach. Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, Menlo Park, CA. pp 229-248.
  4. P. Bose, L. Lietard, O. Pivet, Quantified statements and database fuzzy queries, in: P. Bose, J. Kacprzyk (Eds.), Fuzziness in Database Management Systems, Springer-Verlag, Berlin and Heidelberg, 1995.
  5. V. Cross, T. Sudkamp, Similarity and Compatibility in Fuzzy Set Theory: Assessment and Applications, Springer-Verlag, Heidelberg and New York, 2002.
  6. D.-A. Chiang, L.R. Chqw, Y.-F. Wang (2000). Mining time series data by a fuzzy linguistic summary system. Fuzzy Sets and Systems, 112, pp. 419-432.
  7. G. Das, K. Lin, H. Mannila, G. Renganathan, P. Smyth (1998).. Rule discovery from time series. In Proc. of the 4th Int'l Conference on Knowledge Discovery and Data Mining. New York, NY, pp 16-22.
  8. M. Grabisch, Fuzzy integral as a flexible and interpretable tool of aggregation, in: B. Bouchon-Meunier (Ed.), Aggregation and Fusion of Imperfect Information, Heidelberg, New York: Physica-Verlag, 1998, pp. 51-72.
  9. J. Kacprzyk, A. Wilbik, S. Zadrożny, 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, 2006, pp. 2166-2172, Paris, France, July 2-7, 2006.
  10. J. Kacprzyk, A. Wilbik, S. Zadrożny, Linguistic summaries of time series via a quantifier based aggregation using the Sugeno integral, in: Proceedings of 2006 IEEE World Congress on Computational Intelligence, IEEE Press, 2006, pp. 3610-3616, Vancouver, BC, Canada, July 16-21, 2006.
  11. J. Kacprzyk, A. Wilbik, S. Zadrożny, On some types of linguistic summaries of time series. In: Proceedings of the 3rd International IEEE Conference Intelligent Systems, IEEE Press (2006) pp. 373-378, London, UK, 2006.
  12. J. Kacprzyk, A. Wilbik, S. Zadrożny, 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. Springer-Verlag, Berlin and Heidelberg (2006) pp. 186-190.
  13. J. Kacprzyk, A. Wilbik, S. Zadrożny, 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", IEEE Press (2006) pp. 3365-3370 Taipei, Taiwan, 2006.
  14. J. Kacprzyk, R. R. Yager, Linguistic summaries of data using fuzzy logic, International Journal of General Systems 30 (2001) 33-154.
  15. J. Kacprzyk, R. R. Yager, S. Zadrożny, A fuzzy logic based approach to linguistic summaries of databases, International Journal of Applied Mathematics and Computer Science 10 (2000) 813-834.
  16. J. Kacprzyk, S. Zadrożny, Linguistic database summaries and their protoforms: toward natural language based knowledge discovery tools, Information Sciences 173 (2005) 281-304.
  17. J. Kacprzyk, S. Zadrożny, Fuzzy linguistic data summaries as a human consistent, user adaptable solution to data mining, in: B. Gabryś, K. Leiviska, J. Strackeljan (Eds.), Do Smart Adaptive Systems Exist?, Springer, Berlin, Heidelberg, New York, 2005, pp. 321-339.
  18. E. Keogh, M. Pazzani (1998). An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback. In Proc. of the 4th Int 7 Conference on Knowledge Discovery and Data Mining. New York, NY, pp 239-241.
  19. E. Keogh, K. Chakrabarti, M. Pazzani,S. Mehrotra (2001). Locally adaptive dimensionality reduction for indexing large time series databases. In Proc. of ACM SIGMOD Conference on Management of Data. Santa Barbara, CA, pp 151-162.
  20. J. Sklansky, V. Gonzalez, Fast polygonal approximation of digitized curves, Pattern Recognition 12 (5) (1980) 327-331.
  21. S. Sripada, E. Reiter, I. Davy. SumTime-Mousam: Configurable Marine Weather Forecast Generator. Expert Update 6(3) (2003) 4-10.
  22. R.R. Yager, A new approach to the summarization of data, Information Sciences 28 (1982)69-86.
  23. R.R. Yager, On ordered weighted averaging aggregation operators in multicriteria decision making, IEEE Transactions on Systems, Man and Cybernetics SMC-18 (1988) 183-190.
  24. R.R. Yager, Quantifier guided aggregation using OWA operators, International Journal of Intelligent Systems 11 (1996) 49-73.
  25. R.R. Yager, J. Kacprzyk (1997) The Ordered Weighted Averaging Operators: Theory and Applications. Kluwer, Boston.
  26. L.A. Zadeh, A computational approach to fuzzy quantifiers in natural languages, Computers and Mathematics with Applications 9 (1983) 149-184.
  27. L.A. Zadeh, A prototype-centered approach to adding deduction capabilities to search engines - the concept of a protoform, in: Proceedings of the Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS 2002), 2002, pp. 523-525.
  28. L.A. Zadeh, J. Kacprzyk (eds.) (1992). Fuzzy Logic for the Management of Uncertainty, Wiley, New York.
  29. L.A. Zadeh, J. Kacprzyk (eds.) (1999a). Computing with Words in Information/Intelligent Systems: 1. Foundations. Physica-Verlag, Heidelberg and New York.
  30. L.A. Zadeh, J. Kacprzyk (eds.) (1999b). Computing with Words in Information/Intelligent Systems: 2. Applications. Physica-Verlag, Heidelberg and New York.
Cytowane przez
Udostępnij na Facebooku Udostępnij na Twitterze Udostępnij na Google+ Udostępnij na Pinterest Udostępnij na LinkedIn Wyślij znajomemu