Extended Framework and Evaluation for Multivariate Streaming Anomaly Detection with Machine Learning
Andreas Koch, Michael John Petry, Martin Werner · 2024
Streaming anomaly detection in multivariate time series is an important problem relevant for automatic monitoring of various devices. This paper tackles the problem of streaming anomaly detection by extending a framework for the purpose of incorporating model-based approaches and evaluating previously uncombined methods for a total number of 26 distinct machinelearning-based algorithms. The framework identifies four fundamental components inherent to many streaming anomaly detection algorithms and one or more methods are presented for each component. It is found that a simple and computationally less expensive strategy for detecting concept drift yields almost identical results to the ’’KSWIN’’ strategy, when applied to measuring concept drift in a training set relevant for training a machine learning model. A secondary experiment supports the effectiveness of finetuning a machine learning model after the detection of concept drift for the purpose of detecting anomalies.