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Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification

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arxiv 2002.10061 v3 pith:FSSPGEJQ submitted 2020-02-24 cs.LG stat.ML

Wensi Tang , Guodong Long , Lu Liu , Tianyi Zhou , Michael Blumenstein , Jing Jiang This is my paper
classification cs.LGstat.ML
keywords sizeos-blockseriestimekernelmodelsoptimalperformance
verification ladder T0 review T1 audit T2 compute T3 formal
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The Receptive Field (RF) size has been one of the most important factors for One Dimensional Convolutional Neural Networks (1D-CNNs) on time series classification tasks. Large efforts have been taken to choose the appropriate size because it has a huge influence on the performance and differs significantly for each dataset. In this paper, we propose an Omni-Scale block (OS-block) for 1D-CNNs, where the kernel sizes are decided by a simple and universal rule. Particularly, it is a set of kernel sizes that can efficiently cover the best RF size across different datasets via consisting of multiple prime numbers according to the length of the time series. The experiment result shows that models with the OS-block can achieve a similar performance as models with the searched optimal RF size and due to the strong optimal RF size capture ability, simple 1D-CNN models with OS-block achieves the state-of-the-art performance on four time series benchmarks, including both univariate and multivariate data from multiple domains. Comprehensive analysis and discussions shed light on why the OS-block can capture optimal RF sizes across different datasets. Code available [https://github.com/Wensi-Tang/OS-CNN]

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