DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection

Abstract: Time series anomaly detection is critical for a wide range of applications. It aims to identify deviant samples from the normal sample distribution in time series. The most fundamental challenge for this task is to learn a representation map that enables effective discrimination of anomalies. Reconstruction-based methods still dominate, but the representation learning with anomalies might hurt the performance with its large abnormal loss. On the other hand, contrastive learning aims to find a representation that can clearly distinguish any instance from the others, which can bring a more natural and promising representation for time series anomaly detection. In this paper, we propose DCdetector, a multi-scale dual attention contrastive representation learning model. DCdetector utilizes a novel dual attention asymmetric design to create the permutated environment and pure contrastive loss to guide the learning process, thus learning a permutation invariant representation with superior discrimination abilities. Extensive experiments show that DCdetector achieves state-of-the-art results on multiple time series anomaly detection benchmark datasets. Code is publicly available at https://github.com/DAMO-DI-ML/KDD2023-DCdetector.

• The paper introduces a dual attention contrastive framework that bypasses reconstruction to enhance anomaly detection in time series.
• The methodology leverages a dual-branch design with patch-wise and in-patch attention to capture temporal dependencies and discern anomalies.
• Extensive experiments on eight benchmark datasets demonstrate DCdetector's superior performance and efficiency for real-time applications.

Analyzing DCdetector: A Dual Attention Contrastive Approach for Time Series Anomaly Detection

The paper introduces a novel methodology named DCdetector, designed for time series anomaly detection, using a dual attention contrastive representation learning model. Time series anomaly detection is crucial across various applications where detecting deviations from normal patterns can prevent operational failures and economic losses. The current landscape is dominated by reconstruction-based approaches, which have inherent challenges, especially when anomalies themselves are incorporated into the learning process. DCdetector seeks to address these limitations by leveraging contrastive learning principles.

Methodology

DCdetector operates on the premise that normal and anomalous points will exhibit distinguishable representations due to their inherent patterns. Unlike traditional approaches, DCdetector employs a dual attention design within a contrastive framework to ensure better anomaly discrimination. The model introduces several innovative elements:

1. Dual Attention Mechanism: The dual-branch architecture encapsulates patch-wise and in-patch attention mechanisms. This not only captures temporal dependencies but also highlights representation differences between normal and anomalous data points. Through patching and multi-scale attention design, DCdetector mitigates the information loss typically observed in patch-based models.
2. Contrastive Representation: The model adopts a novel representation learning strategy devoid of reconstruction. This approach effectively widens the gap between normal and anomaly representations.
3. Loss Function and Optimization: The optimization process depends on the KL divergence between patch-wise and in-patch views. DCdetector is free from the traditional reconstruction loss, which usually suffers interference from anomalies, enhancing its robustness.

Experimental Analysis

To establish the credibility of DCdetector, extensive experiments were conducted on eight benchmark datasets, covering both univariate and multivariate time series. The results indicate that DCdetector surpasses or matches state-of-the-art performances, showcasing its efficacy in diverse scenarios with different anomaly types. Moreover, the paper provides comparisons against various models, such as AutoEncoder, OmniAnomaly, and recent representation-based models like Anomaly Transformer. The superior performance highlights the dual attention mechanism's potency in capturing complex time series patterns.

Theoretical and Practical Implications

From a theoretical standpoint, the shift from reconstruction to representation through contrastive learning addresses some critical pitfalls in anomaly detection methodologies. This transition facilitates a more robust model against unforeseen anomaly types and enhances generalization capabilities. The dual attention architecture leverages the intrinsic patterns across both complete and partial views of time series, rendering it versatile in applications where time complexity is a concern.

Practically, DCdetector's approach offers a scalable solution, allowing for real-time processing and anomaly detection in industrial applications, financial fraud detection, and monitoring systems dependent on sensor data. The absence of reconstruction loss also implies minimal computational overhead, enhancing the deployment feasibility in resource-constrained environments.

Future Prospects

The introduction of DCdetector marks a step towards more efficient and effective time series anomaly detection frameworks. Future research could explore the integration of additional attention mechanisms and the expansion of contrastive learning techniques. Moreover, real-world scenarios often deal with evolving datasets; thus, enhancing the adaptability of DCdetector to changing data distributions could be a potential area of progression. Additionally, extending the framework to incorporate dynamic anomaly thresholds might accommodate varying levels of anomaly severity and frequency.

In conclusion, DCdetector signifies an advancement in anomaly detection paradigms by shifting towards contrastive representation learning, backed by a meticulously designed dual attention model. This framework not only addresses current limitations in reconstruction-based methods but also sets a foundation for forthcoming innovations in the time series analysis domain.