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

Li, Zhihan; Zhao, Youjian; Han, Jiaqi; Su, Ya; Jiao, Rui; Wen, Xidao; Pei, Dan

doi:10.1145/3447548.3467075

Cited by 178 publications

(72 citation statements)

References 13 publications

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“…OmniAnomaly proposed by Su et al (2019b) further extends the LSTM-VAE model with a normalizing flow and uses the reconstruction probabilities for detection. InterFusion from Li et al (2021) renovates the backbone to a hierarchical VAE to model the inter-and intra-dependency among multiple series simultaneously. GANs (Goodfellow et al, 2014) are also used for reconstruction-based anomaly detection (Schlegl et al, 2019;Li et al, 2019a;Zhou et al, 2019) and perform as an adversarial regularization.…”

Section: Unsupervised Time Series Anomaly Detectionmentioning

confidence: 99%

“…But these classic methods do not consider the temporal information and are difficult to generalize to unseen real-world scenarios. Benefiting from the great representation learning capability of neural networks, recent deep models (Su et al, 2019b;Shen et al, 2020;Li et al, 2021) have made remarkable advances. They mainly focus on learning temporal representations through well-designed recurrent networks and self-supervised by the reconstruction task, in which the most practical anomaly criterion is reconstruction error per time point based on the learned representations.…”

Section: Introductionmentioning

confidence: 99%

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Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy

Wang

et al. 2021

Preprint

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Unsupervisedly detecting anomaly points in time series is challenging, which requires the model to learn informative representations and derive a distinguishable criterion. Prior methods mainly detect anomalies based on the recurrent network representation of each time point. However, the point-wise representation is less informative for complex temporal patterns and can be dominated by normal patterns, making rare anomalies less distinguishable. We find that in each time series, each time point can also be described by its associations with all time points, presenting as a point-wise distribution that is more expressive for temporal modeling. We further observe that due to the rarity of anomalies, it is harder for anomalies to build strong associations with the whole series and their associations shall mainly concentrate on the adjacent time points. This observation implies an inherently distinguishable criterion between normal and abnormal points, which we highlight as the Association Discrepancy. Technically we propose the Anomaly Transformer with an Anomaly-Attention mechanism to compute the association discrepancy. A minimax strategy is devised to amplify the normal-abnormal distinguishability of the association discrepancy. Anomaly Transformer achieves state-of-the-art performance on six unsupervised time series anomaly detection benchmarks for three applications: service monitoring, space & earth exploration, and water treatment.

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Section: Unsupervised Time Series Anomaly Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy

Wang

et al. 2021

Preprint

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“…Classical methods like [6,14,20,29,39,41] have a fast training speed, but their detection accuracy is not high, due to low expressiveness capability of their model. Using deep learning models, methods like [9,18,28,36] break the records of classical methods with a surprisingly high detection accuracy thanks to high expressiveness of these models. In the research line of modern methods, most efforts were spent on developing sophisticated models to improve detection accuracy [9,18,28,36].…”

Section: Introductionmentioning

confidence: 99%

“…Using deep learning models, methods like [9,18,28,36] break the records of classical methods with a surprisingly high detection accuracy thanks to high expressiveness of these models. In the research line of modern methods, most efforts were spent on developing sophisticated models to improve detection accuracy [9,18,28,36]. As a consequence, these sophisticated models becomes more and more time-consuming for training.…”

Section: Introductionmentioning

confidence: 99%

“…First, the MTS training data is usually not associated with anomalous labels and there is no specification on the pattern of an anomaly in general [4,37]. Second, the anomalous data points in MTS training data can mislead the trained model [18]. Third, the MTS data is usually of high dimension and it has complex spatial and temporal dependence [4,37], where the spatial dependence refers to the dependence among different time series.…”

Section: Introductionmentioning

confidence: 99%

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LPC-AD: Fast and Accurate Multivariate Time Series Anomaly Detection via Latent Predictive Coding

Qi¹,

Xie²,

Li³

et al. 2022

Preprint

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This paper proposes LPC-AD, a fast and accurate multivariate time series (MTS) anomaly detection method. LPC-AD is motivated by the ever-increasing needs for fast and accurate MTS anomaly detection methods to support fast troubleshooting in cloud computing, micro-service systems, etc. LPC-AD is fast in the sense that its reduces the training time by as high as 38.2% compared to the state-of-the-art (SOTA) deep learning methods that focus on training speed. LPC-AD is accurate in the sense that it improves the detection accuracy by as high as 18.9% compared to SOTA sophisticated deep learning methods that focus on enhancing detection accuracy. Methodologically, LPC-AD contributes a generic architecture LPC-Reconstruct for one to attain different trade-offs between training speed and detection accuracy. More specifically, LPC-Reconstruct is built on ideas from autoencoder for reducing redundancy in time series, latent predictive coding for capturing temporal dependence in MTS, and randomized perturbation for avoiding overfitting of anomalous dependence in the training data. We present simple instantiations of LPC-Reconstruct to attain fast training speed, where we propose a simple randomized perturbation method. The superior performance of LPC-AD over SOTA methods is validated by extensive experiments on four large real-world datasets. Experiment results also show the necessity and benefit of each component of the LPC-Reconstruct architecture and that LPC-AD is robust to hyper parameters.

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Slow feature‐constrained decomposition autoencoder: Application to process anomaly detection and localization

Jia,

Jiang,

et al. 2024

Adaptive Control & Signal

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SummaryDetecting anomalies in manufacturing processes is crucial for ensuring safety. However, noise significantly undermines the reliability of data‐driven anomaly detection models. To address this challenge, we propose a slow feature‐constrained decomposition autoencoder (SFC‐DAE) for anomaly detection in noisy scenarios. Considering that the process can exhibit both long‐term trends and periodic properties, the process data is decomposed into trends and cycles. The repetitive information is mitigated by slicing and randomly masking certain trends and cycles. Dependencies among slices are constructed to extract intrinsic information, while high‐frequency noise is reduced using a slow feature‐constrained loss. Anomalies are detected and localized through a reconstruction error strategy. The effectiveness of SFC‐DAE is demonstrated using data from a sugar factory and a secure water treatment system.

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

Cited by 178 publications

References 13 publications

Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy

Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy

LPC-AD: Fast and Accurate Multivariate Time Series Anomaly Detection via Latent Predictive Coding

Slow feature‐constrained decomposition autoencoder: Application to process anomaly detection and localization

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