End-To-End Self-tuning Self-supervised Time Series Anomaly Detection

• URL: http://arxiv.org/abs/2404.02865v1
• Date: Wed, 3 Apr 2024 16:57:26 GMT
• Title: End-To-End Self-tuning Self-supervised Time Series Anomaly Detection
• Authors: Boje Deforce, Meng-Chieh Lee, Bart Baesens, Estefanía Serral Asensio, Jaemin Yoo, Leman Akoglu,
• Abstract summary: Time series anomaly detection (TSAD) finds many applications such as monitoring environmental sensors, industry type, 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. We introduce TSAP for TSA "on autoPilot", which can (self)tune hyper parameters end-to-end.
• Score: 32.746688248671084
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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