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.
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Notes
- 1.
Only the artificial data can be shared: http://public.ostfalia.de/~hoeppnef/tsfilter.html.
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Höppner, F. (2015). Optimal Filtering for Time Series Classification. In: Jackowski, K., Burduk, R., Walkowiak, K., Wozniak, M., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2015. IDEAL 2015. Lecture Notes in Computer Science(), vol 9375. Springer, Cham. https://doi.org/10.1007/978-3-319-24834-9_4
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DOI: https://doi.org/10.1007/978-3-319-24834-9_4
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