A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data (1811.08055v1)

Abstract: Nowadays, multivariate time series data are increasingly collected in various real world systems, e.g., power plants, wearable devices, etc. Anomaly detection and diagnosis in multivariate time series refer to identifying abnormal status in certain time steps and pinpointing the root causes. Building such a system, however, is challenging since it not only requires to capture the temporal dependency in each time series, but also need encode the inter-correlations between different pairs of time series. In addition, the system should be robust to noise and provide operators with different levels of anomaly scores based upon the severity of different incidents. Despite the fact that a number of unsupervised anomaly detection algorithms have been developed, few of them can jointly address these challenges. In this paper, we propose a Multi-Scale Convolutional Recurrent Encoder-Decoder (MSCRED), to perform anomaly detection and diagnosis in multivariate time series data. Specifically, MSCRED first constructs multi-scale (resolution) signature matrices to characterize multiple levels of the system statuses in different time steps. Subsequently, given the signature matrices, a convolutional encoder is employed to encode the inter-sensor (time series) correlations and an attention based Convolutional Long-Short Term Memory (ConvLSTM) network is developed to capture the temporal patterns. Finally, based upon the feature maps which encode the inter-sensor correlations and temporal information, a convolutional decoder is used to reconstruct the input signature matrices and the residual signature matrices are further utilized to detect and diagnose anomalies. Extensive empirical studies based on a synthetic dataset and a real power plant dataset demonstrate that MSCRED can outperform state-of-the-art baseline methods.

• The paper introduces MSCRED, integrating a convolutional encoder and attention-based ConvLSTM to capture both spatial and temporal dependencies.
• The paper demonstrates superior anomaly detection performance, outperforming models on synthetic and real datasets with higher precision, recall, and F1 scores.
• The paper offers actionable insights by pinpointing root causes and assessing anomaly severity through detailed analysis of reconstruction residuals in multi-scale signature matrices.

Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data

The paper introduces a novel framework, MSCRED, for addressing the complex task of anomaly detection and diagnosis within multivariate time series data. This work focuses on effectively capturing both the temporal dependencies across time steps and the inter-correlations among different time series in an unsupervised setting. The proposed method emphasizes robustness to noise and varying levels of anomaly severity, goals that are notoriously challenging in current anomaly detection systems.

Introduction to MSCRED

MSCRED—Multi-Scale Convolutional Recurrent Encoder-Decoder—is designed to capture multiple levels of system status through innovative multi-scale signature matrices. These matrices are fundamental as they encode inter-sensor relationships and temporal patterns, providing a dense representation of system behavior over time. The architecture employs a convolutional encoder for spatial pattern recognition, alongside an attention-based ConvLSTM for the integration of temporal dependencies, eventually reconstructing the signature matrices through a convolutional decoder.

Methodological Advances

Key contributions of MSCRED include the joint formulation of anomaly detection and diagnosis into three interrelated tasks: identifying anomalies, pinpointing root causes, and interpreting anomaly severity. This holistic approach contrasts with previous research, which often dissects these tasks independently.

Furthermore, MSCRED introduces the concept of system signature matrices—unique constructs that capture correlated abnormal behaviors across multiple time series. By leveraging convolutional and recurrent layers, along with attention mechanisms, MSCRED deeply integrates spatial and temporal information to enhance detection fidelity.

Empirical Validation

Empirical results underscore MSCRED's performance superiority. Conducted on both synthetic data and real power plant datasets, experiments reveal that MSCRED significantly outperforms various baselines, including temporal prediction models like LSTM-ED, traditional methods such as ARMA, and density estimation approaches like DAGMM.

Precision, recall, and F1 scores were notably higher, validating the capability of MSCRED to handle the complexities inherent in real-world multivariate time series data, including noise resilience—a critical attribute for practical anomaly detection systems.

Anomaly Analysis and Diagnosis

MSCRED's robust framework allows for precise root cause identification by analyzing the reconstruction residuals of signature matrices. The residual analysis elucidates which time series contributed to the detected anomalies, thus aiding in timely and targeted mitigation actions.

Additionally, by employing multi-scale signature matrices, MSCRED provides insights into anomaly severity based on the duration and scale. This scale-based evaluation enables a layered understanding of anomaly implications, which is essential for actionable insights.

Implications and Future Scope

The implications of this research extend into practical deployments in various industries characterized by complex systems and extensive sensor networks. MSCRED's capability to handle noise and provide nuanced anomaly diagnostics offers substantial potential for real-time monitoring applications across domains as varied as manufacturing, energy, and IT infrastructure.

Future work could focus on further scaling MSCRED for even larger datasets and exploring adaptive mechanisms within the ConvLSTM to refine temporal modeling across evolving system behaviors. Additionally, integrating explainability aspects could enhance the interpretability of anomaly scores, making them more accessible to non-expert stakeholders.

In conclusion, MSCRED represents a significant advancement in unsupervised anomaly detection within multivariate time series, offering a comprehensive and effective toolset for real-world applications.