  Anomaly detection is widely used in network intrusion detection, autonomous
driving, medical diagnosis, credit card frauds, etc. However, several key
challenges remain open, such as lack of ground truth labels, presence of
complex temporal patterns, and generalizing over different datasets. This paper
proposes TSI-GAN, an unsupervised anomaly detection model for time-series that
can learn complex temporal patterns automatically and generalize well, i.e., no
need for choosing dataset-specific parameters, making statistical assumptions
about underlying data, or changing model architectures. To achieve these goals,
we convert each input time-series into a sequence of 2D images using two
encoding techniques with the intent of capturing temporal patterns and various
types of deviance. Moreover, we design a reconstructive GAN that uses
convolutional layers in an encoder-decoder network and employs
cycle-consistency loss during training to ensure that inverse mappings are
accurate as well. In addition, we also instrument a Hodrick-Prescott filter in
post-processing to mitigate false positives. We evaluate TSI-GAN using 250
well-curated and harder-than-usual datasets and compare with 8 state-of-the-art
baseline methods. The results demonstrate the superiority of TSI-GAN to all the
baselines, offering an overall performance improvement of 13% and 31% over the
second-best performer MERLIN and the third-best performer LSTM-AE,
respectively.
