  Recent works have demonstrated the superiority of supervised Convolutional
Neural Networks (CNNs) in learning hierarchical representations from time
series data for successful classification. These methods require sufficiently
large labeled data for stable learning, however acquiring high-quality labeled
time series data can be costly and potentially infeasible. Generative
Adversarial Networks (GANs) have achieved great success in enhancing
unsupervised and semi-supervised learning. Nonetheless, to our best knowledge,
it remains unclear how effectively GANs can serve as a general-purpose solution
to learn representations for time series recognition, i.e., classification and
clustering. The above considerations inspire us to introduce a Time-series
Convolutional GAN (TCGAN). TCGAN learns by playing an adversarial game between
two one-dimensional CNNs (i.e., a generator and a discriminator) in the absence
of label information. Parts of the trained TCGAN are then reused to construct a
representation encoder to empower linear recognition methods. We conducted
comprehensive experiments on synthetic and real-world datasets. The results
demonstrate that TCGAN is faster and more accurate than existing time-series
GANs. The learned representations enable simple classification and clustering
methods to achieve superior and stable performance. Furthermore, TCGAN retains
high efficacy in scenarios with few-labeled and imbalanced-labeled data. Our
work provides a promising path to effectively utilize abundant unlabeled time
series data.
