  Neural networks are capable of learning powerful representations of data, but
they are susceptible to overfitting due to the number of parameters. This is
particularly challenging in the domain of time series classification, where
datasets may contain fewer than 100 training examples. In this paper, we show
that the simple methods of cutout, cutmix, mixup, and window warp improve the
robustness and overall performance in a statistically significant way for
convolutional, recurrent, and self-attention based architectures for time
series classification. We evaluate these methods on 26 datasets from the
University of East Anglia Multivariate Time Series Classification (UEA MTSC)
archive and analyze how these methods perform on different types of time series
data.. We show that the InceptionTime network with augmentation improves
accuracy by 1% to 45% in 18 different datasets compared to without
augmentation. We also show that augmentation improves accuracy for recurrent
and self attention based architectures.
