Abstract
In real applications, time series are generally of complex structure, exhibiting different global behaviors within classes. To discriminate such challenging time series, we propose a multiple temporal matching approach that reveals the commonly shared features within classes, and the most differential ones across classes. For this, we rely on a new framework based on the variance/covariance criterion to strengthen or weaken matched observations according to the induced variability within and between classes. The experiments performed on real and synthetic datasets demonstrate the ability of the multiple temporal matching approach to capture fine-grained distinctions between time series.
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Frambourg, C., Douzal-Chouakria, A., Gaussier, E. (2013). Learning Multiple Temporal Matching for Time Series Classification. In: Tucker, A., Höppner, F., Siebes, A., Swift, S. (eds) Advances in Intelligent Data Analysis XII. IDA 2013. Lecture Notes in Computer Science, vol 8207. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41398-8_18
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DOI: https://doi.org/10.1007/978-3-642-41398-8_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41397-1
Online ISBN: 978-3-642-41398-8
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