A Self-Supervised Learning Framework for Robust Anomaly Detection in Imbalanced and Heterogeneous Time-Series Data

Time-series anomaly detection faces significant challenges when dealing with imbalanced data distributions, distribution shifts, and heterogeneous feature types. Traditional supervised methods struggle due to limited labeled anomaly samples, while unsupervised approaches often produce high false positive rates. We propose a novel self-supervised learning framework that integrates contrastive representation learning with adaptive distribution monitoring and explainable AI techniques. Our framework employs tailored augmentation strategies for time-series data, learns robust representations through contrastive objectives, and utilizes SHAP values to provide interpretable anomaly explanations across heterogeneous features. Experimental results on multiple benchmark datasets demonstrate that our approach achieves an F1-score of 0.823, outperforming state-of-the-art methods by 8.9% while maintaining interpretability and robustness to distribution shifts. The framework effectively handles imbalance ratios up to 1:100 and provides actionable insights for real-world deployment.