Active Learning in Multivariate Time Series Anomaly Detection
Z. Huang. Organic Computing -- Doctoral Dissertation Colloquium 2021, kassel university press, (2022)
Multivariate time series anomaly detection is an important research field of industry, science, and finances.
In practical applications, unlabeled data is abundant, and labeled data is rare.
Experts’ experience plays a key role in data labeling, while experts often have limited time, are expensive, and are concerned primarily with finding the potential anomalies.
Active learning, which has been widely used to select the most informative data for labeling through experts, can improve training with less labeling effort.
However, most active learning algorithms have not been evaluated on datasets with imbalanced class frequencies, such as anomaly detection.
Although some semi-supervised and active learning methods were proposed to handle univariate time series data, not much work has been done on multivariate time series classification.
Therefore, this article identifies critical challenges and proposes potential solutions for active learning in multivariate time series anomaly detection.
We combine all possible solutions into a framework that conforms to the scope of the observation/control system in organic computing.