Explainable Time Series Anomaly Detection using Masked Latent Generative Modeling (2311.12550v5)

Abstract: We present a novel time series anomaly detection method that achieves excellent detection accuracy while offering a superior level of explainability. Our proposed method, TimeVQVAE-AD, leverages masked generative modeling adapted from the cutting-edge time series generation method known as TimeVQVAE. The prior model is trained on the discrete latent space of a time-frequency domain. Notably, the dimensional semantics of the time-frequency domain are preserved in the latent space, enabling us to compute anomaly scores across different frequency bands, which provides a better insight into the detected anomalies. Additionally, the generative nature of the prior model allows for sampling likely normal states for detected anomalies, enhancing the explainability of the detected anomalies through counterfactuals. Our experimental evaluation on the UCR Time Series Anomaly archive demonstrates that TimeVQVAE-AD significantly surpasses the existing methods in terms of detection accuracy and explainability. We provide our implementation on GitHub: https://github.com/ML4ITS/TimeVQVAE-AnomalyDetection.

• The paper introduces TimeVQVAE-AD, a novel method that repurposes masked latent generative modeling to directly predict anomaly scores in a transformed time-frequency domain.
• It enhances explainability by generating counterfactual samples that visualize likely normal states when anomalies are detected.
• Empirical evaluations on the UCR-TSA archive show that TimeVQVAE-AD nearly doubles detection accuracy compared to state-of-the-art methods.

Explainable Time Series Anomaly Detection using Masked Latent Generative Modeling

The paper "Explainable Time Series Anomaly Detection using Masked Latent Generative Modeling" introduces TimeVQVAE-AD, a novel approach to Time Series Anomaly Detection (TSAD) enhancing accuracy and explainability by employing masked generative modeling. The method builds upon TimeVQVAE, a state-of-the-art time series generative model, by repurposing its learned prior for anomaly detection tasks.

Overview

TimeVQVAE-AD innovatively addresses TSAD by introducing masked latent generative modeling in contrast to conventional reconstruction or forecasting methods. Implementing TimeVQVAE's structure, TimeVQVAE-AD leverages a deeply learned prior model, effectively analyzing anomalies across various frequency dimensions. This latent space modeling ensures robust generative properties, allowing explicit handling of anomalies via counterfactual generation—offering explainability through sampling likely normal states when anomalies are detected.

Technical Contributions

The approach makes technical strides on multiple fronts. First, it utilizes TimeVQVAE’s masked generative modeling to predict anomaly scores directly from a learned prior in a transformed time-frequency domain. Here, the dimensional semantics are maintained, aiding in anomaly detection across specific frequency bands, thus enhancing interpretability. By tackling anomalies in discrete latent spaces and allowing semantic preservation of time-frequency dimensions, TimeVQVAE-AD presents a robust anomaly identification mechanism compared to traditional TSAD methods, which often falter due to limitations like anomaly amplitude bias.

Further, TimeVQVAE-AD benefits from enhanced explainability supported by counterfactual analysis. Instead of merely identifying anomalies, the method offers counterfactual sampling wherein likely normal scenarios are drawn from the generative model. This generative capability allows practitioners to visualize what typical data could look like, anchoring trust and insight into detected anomalies via figures and metrics that are inherently visual in time series analysis.

Experimental Evaluation

The empirical results underscore TimeVQVAE-AD's superiority, demonstrated through evaluations on the UCR Time Series Anomaly (UCR-TSA) archive, containing numerous curated datasets designed for fair assessment. TimeVQVAE-AD significantly outperformed contemporary methods. It achieved remarkable detection accuracies, reducing flaws seen in existing benchmarks and showing a nearly twofold increase over second-place methods — Matrix Profile SCRIMP, and discord discovery-based MERLIN++.

The introduction of explainable anomaly detection scores throughout different frequency bands and sizes of temporal segments further reinforces its reliability. Additionally, the paper highlights critical inadequacies in prevalent datasets and scoring protocols that hinder authentic anomaly detection, advancing the need for robust frameworks like TimeVQVAE-AD.

Implications and Future Work

The implications are far-reaching, signifying a pivotal moment in TSAD methodologies. For practitioners, the method offers an enriched, multilayered analysis tool fostering greater understanding and interpretation of anomalies. Theoretically, it points to the potential of masked generative models in extricating nuanced anomalies uniquely across different dimensions.

Future directions could involve extending this approach towards multivariate time series, enriching its application in various sectors such as finance and healthcare. Moreover, exploring parallel compute optimizations, given TimeVQVAE-AD's dependence on processing extensive time series data, remains an operational challenge to address scalability concerns.

In conclusion, TimeVQVAE-AD sets a high benchmark marrying anomaly detection with explainability, propelling developments in artificial intelligence where interpretability is increasingly recognized as valuable. This paper contributes significantly by enhancing detection accuracy and fostering insights that were once obscured in time-frequency analysis.