  Time series anomaly detection (TSAD) finds many applications such as
monitoring environmental sensors, industry KPIs, patient biomarkers, etc. A
two-fold challenge for TSAD is a versatile and unsupervised model that can
detect various different types of time series anomalies (spikes,
discontinuities, trend shifts, etc.) without any labeled data. Modern neural
networks have outstanding ability in modeling complex time series.
Self-supervised models in particular tackle unsupervised TSAD by transforming
the input via various augmentations to create pseudo anomalies for training.
However, their performance is sensitive to the choice of augmentation, which is
hard to choose in practice, while there exists no effort in the literature on
data augmentation tuning for TSAD without labels. Our work aims to fill this
gap. We introduce TSAP for TSA "on autoPilot", which can (self-)tune
augmentation hyperparameters end-to-end. It stands on two key components: a
differentiable augmentation architecture and an unsupervised validation loss to
effectively assess the alignment between augmentation type and anomaly type.
Case studies show TSAP's ability to effectively select the (discrete)
augmentation type and associated (continuous) hyperparameters. In turn, it
outperforms established baselines, including SOTA self-supervised models, on
diverse TSAD tasks exhibiting different anomaly types.
