Abstract
A system to support the mining task of sets of time series is presented. A model of a set of time series is constructed by a series of classifiers each defining certain consecutive time points based on the characteristics of particular time points in the series. Matching a previously unknown series with respect to a model is discussed. The architecture of the MSTS-System (Mning of Sets of Time Series) is described. As a distinctive feature the system is implemented as a database application: time series and the models, i.e. series of classifiers, are database objects. As a consequence of this integration, advanced functionality as the manipulation of models and various forms of meta learning can be easily build on top of MSTS.
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Lausen, G., Savnik, I., Dougarjapov, A. (2000). MSTS: A System for Mining Sets of Time Series. In: Zighed, D.A., Komorowski, J., Żytkow, J. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 2000. Lecture Notes in Computer Science(), vol 1910. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45372-5_28
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DOI: https://doi.org/10.1007/3-540-45372-5_28
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