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Efficient Multivariate Time Series Anomaly Detection through Transfer Learning for Large-Scale Software Systems
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DOI：10.1145/3702984.png)
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Abstract
En 中文
Timely anomaly detection of multivariate time series (MTS) is of vital importance for managing large-scale software systems. However, many deep learning-based MTS anomaly detection models require long-term MTS training data to achieve optimal performance, which often conflicts with the frequent pattern changes observed in software systems. Moreover, the training overhead of vast MTS in large-scale software systems is unacceptably high. To address these issues, we design OmniTransfer, a model-agnostic framework that combines weighted hierarchical agglomerative clustering with an adaptive transfer learning strategy, making many state-of-the-art (SOTA) MTS anomaly detection models efficient and effective. Extensive experiments using real-world data from a large web content service provider and a network operator show that OmniTransfer significantly reduces the model initialization time by 46.49% and the training cost by 74.51%, while maintaining high accuracy in detecting anomalies.
Keywords:
Transfer Learning
Multivariate Time Series
Multivariate Time Series Clustering
Anomaly Detection
Journal
A
IF:
6.2
Papers:
1.2K
Citations:
3.4K