Abstract
Motivated by the great efficiency of dynamic time warping (DTW) for time series similarity measure, a Gaussian DTW (GDTW) kernel has been developed for time series classification. This paper proposes an altered Gaussian DTW (AGDTW) kernel function, which takes into consideration each of warping path between time series. Time series can be mapped into a special kernel space where the homogeneous data gather together and the heterogeneous data separate from each other. Classification results on transformed time series combined with different classifiers demonstrate that the AGDTW kernel is more powerful to represent and classify time series than the Gaussian radius basis function (RBF) and GDTW kernels.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61373093 and 61672364, by the Natural Science Foundation of Jiangsu Province of China under Grant No. BK20140008, and by the Soochow Scholar Project.
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Xue, Y., Zhang, L., Tao, Z., Wang, B., Li, F. (2017). An Altered Kernel Transformation for Time Series Classification. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10638. Springer, Cham. https://doi.org/10.1007/978-3-319-70139-4_46
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DOI: https://doi.org/10.1007/978-3-319-70139-4_46
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