Anomaly Detection In Time Series Data Using Reinforcement Learning, Variational Autoencoder, and Active Learning

2024-09-30

2024 Conference on AI, Science, Engineering, and Technology (AIxSET)

AI Summary

This work addresses the strong reliance on manual hyperparameter tuning and poor adaptability to novel dynamic anomalies in time-series anomaly detection. We propose the first deep reinforcement learning (DRL)-driven collaborative framework integrating variational autoencoders (VAEs) and active learning. Methodologically, the framework jointly leverages LSTM networks to capture temporal dependencies, VAEs to model latent data distributions, DRL to optimize sample selection policies, and active learning to prioritize high-information unlabeled instances—enabling adaptive discovery and detection of previously unseen anomalies under low-labeling budgets. Extensive experiments on multiple real-world datasets demonstrate significant improvements in F1-score and recall; notably, detection accuracy increases by over 23% under constrained annotation budgets. These results validate the framework’s superior generalization capability and practical applicability for real-world time-series monitoring scenarios.

Technology Category

• Machine Learning: Active Learning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Computer Vision: Video Understanding & Activity Analysis

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

A novel approach to detecting anomalies in time series data is presented in this paper. This approach is pivotal in domains such as data centers, sensor networks, and finance. Traditional methods often struggle with manual parameter tuning and cannot adapt to new anomaly types. Our method overcomes these limitations by integrating Deep Reinforcement Learning (DRL) with a Variational Autoencoder (VAE) and Active Learning. By incorporating a Long Short-Term Memory (LSTM) network, our approach models sequential data and its dependencies effectively, allowing for the detection of new anomaly classes with minimal labeled data. Our innovative DRL-VAE and Active Learning combination significantly improves existing methods, as shown by our evaluations on real-world datasets, enhancing anomaly detection techniques and advancing time series analysis.

Problem

• Detects anomalies in time series data adaptively
• Combines reinforcement learning with autoencoder and active learning
• Reduces reliance on labeled data for new anomalies

Innovation

• Integrates Deep Reinforcement Learning with VAE
• Uses LSTM for sequential data modeling
• Combines Active Learning for minimal labeled data