Deep Anomaly Detection on Time Series

• Applicant: Professor Dr. Marius Kloft
• Subject Area: Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing ; Technical Thermodynamics
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 459419731

Time series are ubiquitous, but - in the light of the enormous recent advances enabled by deep learning - anomaly detection (AD) on time series is still in its infancy. In this project, we will develop methods creating modern AD on time series, which is based on deep learning. To this end, we will carry ideas from image AD (such as self-supervised contrastive learning and outlier exposure) over to AD on time series. Such methods rely on a variety of supplemental data (such as the availability of powerful data-augmentation schemes and massive corpora of negative samples) that is notoriously difficult or simply impossible to obtain for time series. As a result, we will study innovative strategies replacing the established supplements. Our approach is based on pragmatically learning the required data augmentations and negative samples. In addition, we will develop methods for AD on time series from the ground up. The proposed model will be based on a random field to model distributions over the gradients of the data. Lastly, we will use contrastive learning to incorporate prior knowledge on chemical processes into our methods, enabling few-shot anomaly detection on chemical process data.

• DFG Programme: Research Units
• Subproject of: FOR 5359: KI-FOR: Deep Learning on Sparse Chemical Process Data
• Cooperation Partner: Dr. Maja Rudolph