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

Understand

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.