HydraMamba: An Efficient and High-Performance Architecture for Time Series Classification through Multi-Mechanism Fusion

Multivariate time series classification is a key task in fields such as healthcare, financial analysis, and industrial monitoring. How- ever, existing methods still face challenges in modeling complex depen- dencies across different time scales, and their computational efficiency is relatively low. To address these issues, we propose an efficient and high- performance model architecture, HydraMamba, which enhances model- ing capability by integrating three core mechanisms: The Time Feature Recalibration Module TFRM adaptively adjusts the feature weights of time segments to improve the model’s ability to focus on key moments the Multi-Receptive Field Feature Extractor MRFFE extracts local and global information in parallel using receptive fields of different sizes, enhancing feature representation and the Dynamic State-Space Mixer DSSM , based on state-space modeling, effectively integrates multi-scale temporal features. We conducted extensive experiments on the UEA multivariate time series classification benchmark datasets, where Hydra- Mamba outperformed mainstream methods like TodyNet on the Heart- beat dataset, achieving an F1 score of 0.898. The experimental results show that HydraMamba maintains high computational efficiency while offering superior classification performance, demonstrating strong gener- alization ability and application potential.