Deep Learning-Based Explainable Anomaly Detection In Multivariate Time Series
Explainable anomaly detection is increasingly vital in high-stakes fields like healthcare, intelligent transport, and cybersecurity. In these areas, understanding and responding to anomalies effectively can have critical implications. Unlike traditional methods that simply identify anomalies, explainable anomaly detection delves deeper, offering insights into the ‘why’ behind these irregularities. This approach is rooted in the analysis of neural networks, identifying key features that signal anomalies. The proposal focuses on advancing explainable anomaly detection in multivariate time series using deep learning techniques. Three innovative directions are proposed to address current gaps in the field: Multivariate Time Series Data Generation with WGAN Framework: This method employs the Wasserstein Generative Adversarial Network (WGAN) framework for generating multivariate time series data. The WGAN framework is known for its stability and ability to produce high-quality synthetic data. By generating realistic multivariate time series data, researchers can better train and test anomaly detection models, leading to more robust and reliable systems. Local Interpretation of Dimensional Features: The proposal addresses the local interpretation problem by analyzing how each dimensional feature of the input contributes to the model’s final decision. This granular analysis enhances the understanding of which specific aspects of the data are most indicative of anomalies. This approach is critical in complex systems where numerous features interact, and identifying the most relevant ones can significantly improve anomaly detection accuracy and reliability. Generative Dual Network (GDN) Based on Teacher-Student Framework: The GDN model introduces a novel approach by implementing a teacher-student framework. This framework allows for the transfer and accumulation of knowledge over time. It is particularly effective in handling multimodal and multi-source data, a common challenge in anomaly detection. The GDN can adapt and learn from a variety of data sources, enhancing its ability to detect anomalies in complex and evolving environments. The proposal’s overarching goal is to enhance the interpretability and effectiveness of anomaly detection in multivariate time series. By integrating advanced deep learning techniques and innovative frameworks like WGAN and GDN, it opens new avenues for research in this critical field. This approach not only promises improvements in detecting anomalies but also in understanding their underlying causes, which is essential for timely and effective intervention in high-stakes situations.