Abstract
Time series classification, due to its applications in various domains, is one of the most important data-driven decision tasks of artificial intelligence. Recent results show that the simple nearest neighbor method with an appropriate distance measure performs surprisingly well, outperforming many state-of-the art methods. This suggests that the choice of distance measure is crucial for time series classification. In this paper we shortly review the most important distance measures of the literature, and, as major contribution, we propose a framework that allows fusion of these different similarity measures in a principled way. Within this framework, we develop a hybrid similarity measure. We evaluate it in context of time series classification on a large, publicly available collection of 35 real-world datasets and we show that our method achieves significant improvements in terms of classification accuracy.
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Buza, K., Nanopoulos, A., Schmidt-Thieme, L. (2011). Fusion of Similarity Measures for Time Series Classification. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds) Hybrid Artificial Intelligent Systems. HAIS 2011. Lecture Notes in Computer Science(), vol 6679. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21222-2_31
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DOI: https://doi.org/10.1007/978-3-642-21222-2_31
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