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 (Mining 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.
CITATION STYLE
Lausen, G., Savnik, I., & Dougarjapov, A. (2000). MSTS: A system for mining sets of time series. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1910, pp. 289–298). Springer Verlag. https://doi.org/10.1007/3-540-45372-5_28
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