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Optimal Filtering for Time Series Classification

  • Frank HöppnerEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9375)

Abstract

The application of a (smoothing) filter is common practice in applications where time series are involved. The literature on time series similarity measures, however, seems to completely ignore the possibility of applying a filter first. In this paper, we investigate to what extent the benefit obtained by more complex distance measures may be achieved by simply applying a filter to the original series (while sticking to Euclidean distance). We propose two ways of deriving an optimized filter from classified time series to adopt the similarity measure to a given application. The empirical evaluation shows not only that in many cases a substantial fraction of the performance improvement can also be achieved by filtering, but also that for certain types of time series this simple approach outperforms more complex measures.

Keywords

Time Series Dynamic Time Warping Original Series Optimal Filter Filter Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer ScienceOstfalia University of Applied SciencesWolfenbüttelGermany

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