Active Learning and Transfer Learning for Anomaly Detection in Time-Series Data

TL;DR

This study explores how combining active learning and transfer learning impacts anomaly detection in time-series data, revealing that active learning offers limited but steady improvements and that clustering may not always be beneficial.

Contribution

It provides new insights into the interaction between clustering, active learning, and transfer learning in time-series anomaly detection, with an improved experimental design and analysis of performance limits.

Findings

Active learning improves model performance but at a slower rate than previously reported.

Clustering does not significantly enhance performance and may be unnecessary.

Performance gains plateau as more target points are added, indicating a limit to active learning benefits.

Abstract

This paper examines the effectiveness of combining active learning and transfer learning for anomaly detection in cross-domain time-series data. Our results indicate that there is an interaction between clustering and active learning and in general the best performance is achieved using a single cluster (in other words when clustering is not applied). Also, we find that adding new samples to the training set using active learning does improve model performance but that in general, the rate of improvement is slower than the results reported in the literature suggest. We attribute this difference to an improved experimental design where distinct data samples are used for the sampling and testing pools. Finally, we assess the ceiling performance of transfer learning in combination with active learning across several datasets and find that performance does initially improve but eventually begins to tail off as more target points are selected for inclusion in training. This tail-off in performance may indicate that the active learning process is doing a good job of sequencing data points for selection, pushing the less useful points towards the end of the selection process and that this tail-off occurs when these less useful points are eventually added. Taken together our results indicate that active learning is effective but that the improvement in model performance follows a linear flat function concerning the number of points selected and labelled.