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A Reproducing Kernel Hilbert Space Framework for Pairwise Time Series Distances
This video was recorded at 25th International Conference on Machine Learning (ICML), Helsinki 2008. A good distance measure for time series needs to properly incorporate the temporal structure, and should be applicable to sequences with unequal lengths. In this paper, we propose a distance measure as a principled solution to the two requirements. Unlike the unconventional feature vector representation, our approach represents each time series with a summarizing smooth curve in a reproducing kernel Hilbert space (RKHS), and therefore translate the distance between time series into distances between curves. Moreover we propose to learn the kernel of this RKHS from a population of time series with discrete observations using Gaussian process-based non-parametric mixed-effect models. Experiments on two vastly different real-world problems show that the proposed distance measure leads to improved classification accuracy over the conventional distance measures.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
