1 / 12

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

violetd
Télécharger la présentation

Mining Time Series Using Rough Sets - a Case Study

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related