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Mining Time Series Using Rough Sets - a Case Study

Mining Time Series Using Rough Sets - a Case Study. Anders Torvill Bjorvand Troll Data Inc. torvill@trolldata.no. Objective. We want to reason about sequences of events Including historic attributes increases the number of attributes initially

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Mining Time Series Using Rough Sets - a Case Study

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  1. Mining Time Series Using Rough Sets - a Case Study Anders Torvill Bjorvand Troll Data Inc. torvill@trolldata.no

  2. Objective • We want to reason about sequences of events • Including historic attributes increases the number of attributes initially • Is it possible to obtain shorter reducts from this approach?

  3. Temporal Information Systems • A Temporal Information System, TIS, is a regular Information System, IS, where one of the attributes are designated as a sequence variable

  4. TIS to IS - I • We have to transform our TIS to an IS in order to effectively reason with sequences • Based on how many time periods we are interested in going back, we create new attributes representing the normalized change between each subsequent period of time

  5. TIS to IS - II • Lets say that we have some market data with 100 sequential measurements spanning 30 attributes • 1 month back  99 objects, 30 attributes • 2 months back  98 objects, 60 attributes • 3 months back  97 objects, 90 attributes • ...

  6. TIS to IS - III

  7. Data Reduction Potential • Both increasing the number of attributes and decreasing the number of original objects gives us a potential for shorter reducts • Is this true only in theory, or will we observe this with real world data ?

  8. Experiment • We computed the shortest reduct of two transformations of stock market data (W. Ziarko, 120 obj. ~30 attr.) • 1 month back : 6 attributes • 2 months back : 4 attributes

  9. Future Work - I • Compose further experiments - to verify/evaluate the theory both qualitatively and quantitatively • Prediction of missing values • Scaling of time intervals for the real time approach • Handling of branching time structure

  10. Future Work - II • Jbin - OODBMS in Java™ • Emphasis on the distributed aspects of KDD • Persistent storage of objects conforming to the JavaBean™ component standard • Mining integrated in the kernel through the engines of Rough Enough and Neuromania

  11. Future Work - III • Synthesis of embedded decision support systems • Means: An algorithm mapping from rule-sets to Java™-code conforming to the EmbeddedJava™ API • Goals: Obtaining support for mobile computing units comforming to the JavaOS™ specifications • Domestic appliances • Cellular phones, etc.

  12. Available Papers • SCAI’97: ‘Rough Enough’ - A System Supporting the Rough Sets Approach • IMACS’97 - Rough Set session: Practical Applications of Genetic Algorithms for Efficient Reduct Computation • IMACS’97 - Rough Set session: ‘Rough Enough’ - Software Demonstration • Master’s thesis: Time Series and Rough Sets Also available at: http://home.sn.no/~torvill

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