Multivariate Time Series Anomaly Detection and Interpretation using Hierarchical Inter-Metric and Temporal Embedding

By Zhihan Li, Youjian Zhao, Jiaqi Han, Ya Su, Rui Jiao, Xidao Wen, Dan Pei. Knowledge Discovery and Data Mining, 2021

497 citations recorded by the source corpus.

Anomaly detection is a crucial task for monitoring various status (i.e., metrics) of entities (e.g., manufacturing systems and Internet services), which are often characterized by multivariate time series (MTS). In practice, it's important to precisely detect the anomalies, as well as to interpret the detected anomalies through localizing a group of most anomalous metrics, to further assist the failure troubleshooting. In this paper, we propose InterFusion, an unsupervised method that simultaneously models the inter-metric and temporal dependency for MTS. Its core idea is to model the normal patterns inside MTS data through hierarchical Variational AutoEncoder with two stochastic latent variables, each of which learns low-dimensional inter-metric or temporal embeddings. Furthermore, we propose an MCMC-based method to obtain reasonable embeddings and reconstructions at anomalous parts for MTS anomaly interpretation. Our evaluation experiments are conducted on four real-world datasets from different industrial domains (three existing and one newly published dataset collected through our pilot deployment of InterFusion). InterFusion achieves an average anomaly detection F1-Score higher than 0.94 and anomaly interpretation performance of 0.87, significantly outperforming recent state-of-the-art MTS anomaly detection methods.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/190a980c2432d8b7e495747ca8e70db0c6ae1ee6

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In Artificial Intelligence

1. Mingsheng Long
2. Jianmin Wang
3. Haixu Wu
4. Yiyuan Yang
5. Liang Sun
6. Jiehui Xu
7. Chaoli Zhang
8. Qingsong Wen

In Computer Networks and Communications

1. Haixu Wu
2. Liang Sun
3. Jiehui Xu
4. Jianmin Wang
5. Qingsong Wen
6. Mingsheng Long
7. Tian Zhou

In Signal Processing

1. Tian Zhou
2. Liang Sun
3. Haixu Wu
4. Mingsheng Long
5. Qingsong Wen
6. Jianmin Wang
7. Yiyuan Yang
8. Jiehui Xu

Citations in the corpus

• DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection (2023, methodology): “We compare our model with the 26 baselines for comprehensive evaluations, including the reconstruction-based model: AutoEn-coder [61], LSTM-VAE [53], OmniAnomaly [66], BeatGAN [91], InterFusion [45], Anomaly Transformer [75]; the autoregression-based models: VAR [4], Autoregression [58], LSTM-RNN [8”
• Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy (2021, background): “Benefiting from the representation learning capability of neural networks, recent deep models (Su et al., 2019; Shen et al., 2020; Li et al., 2021) have achieved superior performance.”
• Deep Learning for Time Series Anomaly Detection: A Survey (2022, unknown): “To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.”