Time Series Anomaly Detection Based on Entropy-Sparsified Time-Frequency Fusion and MsRwGWO Meta-Optimization

Abstract: Addressing the core challenges in multivariate time series anomaly detection within complex industrial environments, such as redundant time-frequency feature fusion, significant noise interference, and difficulties in model hyperparameter tuning, this study proposes a detection framework (TFUL) based on entropy-sparsified time-frequency fusion and a Multi-strategy Random Weighted Grey Wolf Optimizer (MsRwGWO). The main contributions of this work include: 1) A dual-domain entropy sparsification fusion mechanism is designed, which dynamically evaluates and filters crucial temporal segments and frequency components via information entropy, enabling adaptive and redundancy-resistant feature fusion. 2) A heterogeneously collaborative feature extraction network is constructed. The temporal branch, SoftShapeNet, integrates multi-scale convolutions and a Mixture of Experts (MoE) to capture local polymorphic shapes, while the frequency branch, FrequencyDomainProcessor, employs a learnable Mahalanobis distance to model nonlinear spectral dependencies among channels, surpassing the limitations of fixed transformations. 3) The MsRwGWO meta-optimization strategy is proposed, which incorporates dynamic weighting and multi-strategy perturbation mechanisms, significantly enhancing the efficiency and quality of hyperparameter search. Experiments conducted on several public datasets demonstrate that the pro-posed method outperforms mainstream comparative models in terms of detection accuracy and robustness, providing an effective solution for industrial time series anomaly detection.

Xiaogang Yuan, Jiaxi Chen, Dezhi An and Jianxin Wan. “Time Series Anomaly Detection Based on Entropy-Sparsified Time-Frequency Fusion and MsRwGWO Meta-Optimization”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.2 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170299

@article{Yuan2026,
title = {Time Series Anomaly Detection Based on Entropy-Sparsified Time-Frequency Fusion and MsRwGWO Meta-Optimization},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170299},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170299},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {2},
author = {Xiaogang Yuan and Jiaxi Chen and Dezhi An and Jianxin Wan}
}