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Jiffy: A Convolutional Approach to Learning Time Series Similarity
Divya Shanmugam, Davis Blalock, John Guttag
Feb 15, 2018 (modified: Feb 15, 2018)ICLR 2018 Conference Blind Submissionreaders: everyoneShow Bibtex
Abstract:Computing distances between examples is at the core of many learning algorithms for time series. Consequently, a great deal of work has gone into designing effective time series distance measures. We present Jiffy, a simple and scalable distance metric for multivariate time series. Our approach is to reframe the task as a representation learning problem---rather than design an elaborate distance function, we use a CNN to learn an embedding such that the Euclidean distance is effective. By aggressively max-pooling and downsampling, we are able to construct this embedding using a highly compact neural network. Experiments on a diverse set of multivariate time series datasets show that our approach consistently outperforms existing methods.
TL;DR:Jiffy is a convolutional approach to learning a distance metric for multivariate time series that outperforms existing methods in terms of nearest-neighbor classification accuracy.
Keywords:Time Series, Time Series Classification
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