Abstract
In the last years, there is a huge increase of interest in application of time series. Virtually all human endeavors create time-oriented data, and the Data Mining community has proposed a large number of approaches to analyze such data. One of the most common tasks in Data Mining is classification, in which each time series should be associated to a class. Empirical evidence has shown that the nearest neighbor rule is very effective to classify time series data. However, the nearest neighbor classifier is unable to provide any form of explanation. In this chapter we describe a novel method to induce classifiers from time series data. Our approach uses standard Machine Learning classifiers using motifs and characteristics as features. We show that our approach can be very effective for classification, providing higher accuracy for most of the data sets used in an empirical evaluation. In addition, when used with symbolic models, such as decision trees, our approach provides very compact decision rules, leveraging knowledge discovery from time series. We also show two case studies with real world medical data.
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Maletzke, A.G. et al. (2014). Time Series Classification with Motifs and Characteristics. In: Espin, R., Pérez, R., Cobo, A., Marx, J., Valdés, A. (eds) Soft Computing for Business Intelligence. Studies in Computational Intelligence, vol 537. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53737-0_8
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DOI: https://doi.org/10.1007/978-3-642-53737-0_8
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