  While one-dimensional convolutional neural networks (1D-CNNs) have been
empirically proven effective in time series classification tasks, we find that
there remain undesirable outcomes that could arise in their application,
motivating us to further investigate and understand their underlying
mechanisms. In this work, we propose a Temporal Convolutional Explorer (TCE) to
empirically explore the learning behavior of 1D-CNNs from the perspective of
the frequency domain. Our TCE analysis highlights that deeper 1D-CNNs tend to
distract the focus from the low-frequency components leading to the accuracy
degradation phenomenon, and the disturbing convolution is the driving factor.
Then, we leverage our findings to the practical application and propose a
regulatory framework, which can easily be integrated into existing 1D-CNNs. It
aims to rectify the suboptimal learning behavior by enabling the network to
selectively bypass the specified disturbing convolutions. Finally, through
comprehensive experiments on widely-used UCR, UEA, and UCI benchmarks, we
demonstrate that 1) TCE's insight into 1D-CNN's learning behavior; 2) our
regulatory framework enables state-of-the-art 1D-CNNs to get improved
performances with less consumption of memory and computational overhead.
