Unsupervised Online Anomaly Detection in Multivariate Time-Series

Abstract: This research aims to identify a method for unsupervised online anomaly detection in multivariate time series in dynamic systems in general and on the case study of Devwards IoT-system in particular. A requirement of the solution is its explainability, online learning and low computational expense. A comprehensive literature review was conducted, leading to the experimentation and analysis of various anomaly detection approaches. Of the methods evaluated, a singular recurrent neural network autoencoder emerged as the most promising, emphasizing a simple model structure that encourages stable performance with consistent outputs, regardless of the average output. While other approaches such as Hierarchical Temporal Memory models and an ensemble strategy of adaptive model pooling yielded suboptimal results. A modified version of the Residual Explainer method for enhancing explainability in autoencoders for online scenarios showed promising outcomes. The use of Mahalanobis distance for anomaly detection was explored. Feature extraction and it's implications in the context of the proposed approach is explored. Conclusively, a single, streamlined recurrent neural network appears to be the superior approach for this application, though further investigation into online learning methods is warranted. The research contributes results into the field of unsupervised online anomaly detection in multivariate time series and contributes to the Residual Explainer method for online autoencoders. Additionally, it offers data on the ineffectiveness of the Mahalanobis distance in an online anomaly detection environment.