Skip to main content
Springer Nature Link
Log in

A Multi-criteria Evaluation of Linguistic Summaries of Time Series via a Measure of Informativeness

  • Conference paper
Artificial Intelligence and Soft Computing (ICAISC 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6113))

Included in the following conference series:

Abstract

We extend our works of deriving linguistic summaries of time series using a fuzzy logic approach to linguistic summarization. We proceed towards a multicriteria analysis of summaries by assuming as a quality criterion Yager’s measure of informativeness that combines in a natural way the measures of truth, focus and specificity, to obtain a more advanced evaluation of summaries. The use of the informativeness measure for the purpose of a multicriteria evaluation of linguistic summaries of time series seems to be an effective and efficient approach, yet simple enough for practical applications. Results on the summarization of quotations of an investment (mutual) fund are very encouraging.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Kacprzyk, J., Wilbik, A., Zadrożny, S.: Linguistic summarization of time series using a fuzzy quantifier driven aggregation. Fuzzy Sets and Systems 159(12), 1485–1499 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  2. Kacprzyk, J., Wilbik, A.: Linguistic summarization of time series using fuzzy logic with linguistic quantifiers: a truth and specificity based approach. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 241–252. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  3. Kacprzyk, J., Wilbik, A.: Linguistic summarization of time series using linguistic quantifiers: augmenting the analysis by a degree of fuzziness. In: Proceedings of 2008 IEEE WCCI, pp. 1146–1153. IEEE Press, Los Alamitos (2008)

    Google Scholar 

  4. Kacprzyk, J., Wilbik, A.: A new insight into the linguistic summarization of time series via a degree of support: elimination of infrequent patterns. In: Dubois, D., et al. (eds.) Soft Methods for Handling Variability and Imprecision, pp. 393–400. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  5. Kacprzyk, J., Wilbik, A.: Towards an efficient generation of linguistic summaries of time series using a degree of focus. In: Proceedings of NAFIPS 2009 (2009)

    Google Scholar 

  6. Kacprzyk, J., Zadrożny, S.: FQUERY for Access: fuzzy querying for a windows-based dbms. In: Bosc, P., et al. (eds.) Fuzziness in Database Management Systems, pp. 415–433. Springer, Heidelberg (1995)

    Google Scholar 

  7. Kacprzyk, J., Zadrożny, S.: Linguistic database summaries and their protoforms: toward natural language based knowledge discovery tools. Information Sciences 173, 281–304 (2005)

    Article  MathSciNet  Google Scholar 

  8. Past performance does not predict future performance, http://www.freemoneyfinance.com/2007/01/past_performanc.html

  9. Past performance is not everything, http://www.personalfn.com/detail.asp?date=9/1/2007&story=3

  10. New year’s eve: past performance is no indication of future return stockcasting, blogspot.com/2005/12/new-years-evepast-performance-is-no.html

  11. Myers, R.: Using past performance to pick mutual funds. Nation’s Business (October 1997), findarticles.com/p/articles/mi_m1154/is_n10_v85/ai_19856416

  12. Bogle, J.C.: Common Sense on Mutual Funds: New Imperatives for the Intelligent Investor. Wiley, New York (1999)

    Google Scholar 

  13. Securities, U., Commission, E.: Mutual fund investing: Look at more than a fund’s past performance, http://www.sec.gov/investor/pubs/mfperform.htm

  14. Yager, R.R.: A new approach to the summarization of data. Information Sciences 28, 69–86 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  15. Yager, R.R.: On linguistic summaries in data. In: Piatetsky-Shapiro, G., et al. (eds.) Knowledge Discovery in Databases, pp. 347–363. MIT Press, Cambridge (1991)

    Google Scholar 

  16. Kacprzyk, J., Zadrożny, S.: Data mining via protoform based linguistic summaries: some possible relations to natural language generation. In: 2009 IEEE Symposium Series on Computational Intelligence Proceedings, pp. 217–224 (2009)

    Google Scholar 

  17. Sklansky, J., Gonzalez, V.: Fast polygonal approximation of digitized curves. Pattern Recognition 12(5), 327–331 (1980)

    Article  Google Scholar 

  18. Keogh, E., Chu, S., Hart, D., Pazzani, M.: An online algorithm for segmenting time series. In: Proceedings of the 2001 IEEE ICDM (2001)

    Google Scholar 

  19. Keogh, E., Chu, S., Hart, D., Pazzani, M.: Segmenting time series: A survey and novel approach. In: Last, M., et al. (eds.) Data Mining in Time Series Databases, World Scientific Publishing, Singapore (2004)

    Google Scholar 

  20. Kacprzyk, J., Yager, R.R.: Linguistic summaries of data using fuzzy logic. International Journal of General Systems 30, 33–154 (2001)

    Article  MathSciNet  Google Scholar 

  21. 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)

    MATH  Google Scholar 

  22. Kacprzyk, J., Yager, R.R., Zadrożny, S.: Fuzzy linguistic summaries of databases for an efficient business data analysis and decision support. In: Abramowicz, W. (ed.) Knowledge Discovery for Business Information Systems, pp. 129–152. Kluwer, Dordrecht (2001)

    Google Scholar 

  23. Zadeh, L.A.: Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems 9(2), 111–127 (1983)

    MathSciNet  Google Scholar 

  24. Yager, R.R., Ford, K.M., Cañas, A.J.: An approach to the linguistic summarization of data, pp. 456–468. Springer, Heidelberg (1990)

    Google Scholar 

  25. Sripada, S., Reiter, E., Davy, I.: Sumtime-mousam: Configurable marine weather forecast generator. Expert Update 6(3), 4–10 (2003)

    Google Scholar 

  26. Kacprzyk, J., Zadrożny, S.: Computing with words is an implementable paradigm: fuzzy queries, linguistic data summaries and natural language generation. IEEE Transactions on Fuzzy Systems (forthcoming)

    Google Scholar 

  27. 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)

    MATH  Google Scholar 

  28. Batyrshin, I., Sheremetov, L.: Perception based functions in qualitative forecasting. In: Batyrshin, I., et al. (eds.) Perception-based Data Mining and Decision Making in Economics and Finance. Springer, Heidelberg (2006)

    Google Scholar 

  29. Zadeh, L.A.: A prototype-centered approach to adding deduction capabilities to search engines – the concept of a protoform. In: Proceedings of NAFIPS 2002, pp. 523–525 (2002)

    Google Scholar 

  30. Kacprzyk, J., Wilbik, A., Zadrożny, S.: Linguistic summarization of time series under different granulation of describing features. In: Kryszkiewicz, M., Peters, J.F., Rybiński, H., Skowron, A., et al. (eds.) RSEISP 2007. LNCS (LNAI), vol. 4585, pp. 230–240. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  31. Yager, R.R.: On measures of specificity. In: Kaynak, O., et al. (eds.) Computational Intelligence: Soft Computing and Fuzzy-Neuro Integration with Applications, pp. 94–113. Springer, Heidelberg (1998)

    Google Scholar 

  32. Yager, R.R.: Measuring tranquility and anxiety in decision making: An application of fuzzy sets. International Journal of General Systems 8, 139–146 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  33. Zadeh, L.A.: Computation with imprecise probabilities. In: IPMU 2008 (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Anna Wilbik & Janusz Kacprzyk

Authors
  1. Anna Wilbik
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Janusz Kacprzyk
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Academy of Humanities and Economics, Poland

    Leszek Rutkowski

  2. Academy of Humanities and Economics in Łódź,, ul. Rewolucji 1905 nr 64, Łódź, Poland

    Rafał Scherer

  3. Institute of Automatics,, AGH University of Science and Technology, Al. Mickiewicza 30, PL-30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  4. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Initiative in Soft Computing (BISC), 94720-1776, Berkeley, CA,  

    Lotfi A. Zadeh

  5. Computational Intelligence Laboratory Department of Electrical and Computer Engineering, University of Louisville, 40292, Louisville, KY

    Jacek M. Zurada

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Wilbik, A., Kacprzyk, J. (2010). A Multi-criteria Evaluation of Linguistic Summaries of Time Series via a Measure of Informativeness. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2010. Lecture Notes in Computer Science(), vol 6113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13208-7_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-13208-7_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13207-0

  • Online ISBN: 978-3-642-13208-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics