C-GATS: Conditional generation of anomalous time series
Published in Neural Information Processing Systems (NeurIPS), SyntheticData4ML Workshop, 2022
Vikramank Singh, Abishek Sankararaman, Balakrishnan Murali Narayanaswamy, Zhao Song
Sparsity of the data needed to learn about the anomalies is often a key challenge that is faced when it comes to training deep supervised models for the task of Anomaly Detection (AD). Generating synthetic data by applying pre-determined transformations that conform to a set of known invariances has shown to improve performance of such deep models. In this work we present C-GATS to show that one can learn a much larger invariance space using the available sparse data by training a conditional generative model to do Data Augmentation (DA) for anomalous Time Series (TS) in a model-agnostic way. Particularly, we factorize an anomalous TS sequence into 3 attributes— normal sub-sequence, anomalous sub-sequence, and position of the anomaly and model each of them separately. This factorization helps exploit samples from the dominant class i.e normal TS to train a generative model for the sparse class i.e anomalous TS. We provide an exhaustive study to showcase that C-GATS not only learns to generate different types of anomalies (eg: point anomalies and level-shift) but those generated anomalies improve performance of multiple SOTA TS AD models on a set of popular public TS AD benchmark datasets.