Mining time series using rough sets — A case study

  • Anders Torvill Bjorvand
Poster Session 6
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1263)


This article attempts to deal with the problem of time within the framework of rough sets. The rough set theory has emphasized the reduction of information necessary to acquire desired knowledge. This is particularly important when we are dealing with time. The farther back we are tracing our dependencies, the more attributes will become independent of our current decisions.

We formalize approaches to reasoning with time series where the sequence of events is important, and introduce formalisms to deduce decision rules with real-time constraints.


  1. [1]
    Bazan, Jan G., Skowron, Andrzej, and Synak, Piotr (1994). Market Data Analysis: A Rough Set Approach. Technical report from the University of Warsaw.Google Scholar
  2. [2]
    Bjorvand, Anders Torvill (1996). Time Series and Rough Sets. Master's Thesis, the Norwegian Institute of Technology, Department of Computer Systems, Trondheim, Norway.Google Scholar
  3. [3]
    Bjorvand, Anders Torvill and Komorowski, Jan (1997). Practical Applications of Genetic Algorithms for Efficient Reduct Computation. 15th IMACS World Congress 1997 on Scientific Computation, Modelling and Applied Mathematics — to appear.Google Scholar
  4. [4]
    Bjorvand, Anders Torvill (1997). Rough Enough — Software Demonstration. 15th IMACS World Comgress 1997 on Scientific Computation, Modelling and Applied Mathematics — to appear.Google Scholar
  5. [5]
    Golan, Robert and Edwards, Donald (1993). “Temporal Rules Discovery using Datalogic/R+with Stock Market Data”. Printed in Rough Sets, Fuzzy Sets and Knowledge Discovery. Edited by Ziarko, Wojciech P. From the Workshops In Computing series edited by van Rijsbergen, C. J. Pp. 74–81. Springer-Verlag.Google Scholar
  6. [6]
    Golan, Robert and Ziarko, Wojciech (1995). A Methodology for Stock Market Analysis Using Rough Set Theory. Printed in Proceedings of IEEE/IAFE Conference on Computational Intelligence for Fiancial Engineering, New York City, pp. 32–40.Google Scholar
  7. [7]
    Janacek, Gareth and Swift, Louis (1993). Time Series — Forecasting, Simulation, Applications. Ellis Horwood.Google Scholar
  8. [8]
    Ostroff, Jonathan S. (1989). Temporal Logic for Real-Time Systems. Research Studies Press LTD, John Wiley & Sons Inc.Google Scholar
  9. [9]
    Pawlak, Zdzislaw (1991). Rough Sets — Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers, Dordrecht.Google Scholar
  10. [10]
    Skowron, Andrzej (1995). “Synthesis of Adaptive Decision Systems from Experimental Data”. Printed in SCAI'95 — Proceedings of the Fifth Scandinavian Conference on Artificial Intelligence. Edited by A. Aamodt and J. Komorowski. IOS Press.Google Scholar
  11. [11]
    Zakowski, W. (1993). Sequences of Information Systems, Configurations and Conflicts. Bull. Pol. Acad. Sci. Ser., Tech., 41, 295–304.Google Scholar
  12. [12]
    Ziarko, W., Golan, R. and Edwards, D. (1993). An application of Dat-alogic/R Knowledge Discovery Tool to Identify Strong Predictive Rules In Stock Market Data. AAAI-93 Workshop on Knowledge Discovery in Databases.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Anders Torvill Bjorvand
    • 1
  1. 1.Troll Data Inc.AskimNorway

Personalised recommendations