  Deep learning models utilizing convolution layers have achieved
state-of-the-art performance on univariate time series classification tasks. In
this work, we propose improving CNN based time series classifiers by utilizing
Octave Convolutions (OctConv) to outperform themselves. These network
architectures include Fully Convolutional Networks (FCN), Residual Neural
Networks (ResNets), LSTM-Fully Convolutional Networks (LSTM-FCN), and Attention
LSTM-Fully Convolutional Networks (ALSTM-FCN). The proposed layers
significantly improve each of these models with minimally increased network
parameters. In this paper, we experimentally show that by substituting
convolutions with OctConv, we significantly improve accuracy for time series
classification tasks for most of the benchmark datasets. In addition, the
updated ALSTM-OctFCN performs statistically the same as the top two time series
classifers, TS-CHIEF and HIVE-COTE (both ensemble models). To further explore
the impact of the OctConv layers, we perform ablation tests of the augmented
model compared to their base model.
