  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]
