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

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.

Self-Supervised Anomaly Detection of Rogue Soil Moisture Sensors

IoT data is a central element in the successful digital transformation of agriculture. However, IoT data comes with its own set of challenges. E.g., the risk of data contamination due to rogue sensors. A sensor is considered rogue when it provides incorrect measurements over time. To ensure correct analytical results, an essential preprocessing step when working with IoT data is the detection of such rogue sensors. Existing methods assume that well-behaving sensors are known or that a large majority of the sensors is well-behaving. However, real-world data is often completely unlabeled and voluminous, calling for self-supervised methods that can detect rogue sensors without prior information. We present a self-supervised anomalous sensor detector based on a neural network with a contrastive loss, followed by DBSCAN. A core contribution of our paper is the use of Dynamic Time Warping in the negative sampling for the triplet loss. This novelty makes the use of triplet networks feasible for anomalous sensor detection. Our method shows promising results on a challenging dataset of soil moisture sensors deployed in multiple pear orchards.