用哈达玛向量做卷积核，让时间序列分类更快更准
HIT-ROCKET: Hadamard-vector Inner-product Transformer for ROCKET
- 用哈达玛矩阵的行/列向量作卷积核，自动适配不同长度
- 比ROCKET提升5%准确率，训练速度加快50%
- 适合嵌入式设备部署，代码开源可用
时间序列分类在通信、金融、医疗等领域应用广泛。现有先进方法如HIVE-COTE、Proximity Forest和TS-CHIEF存在计算复杂度高、调参耗时等问题。轻量级方案如ROCKET虽高效，但在核选择和计算开销方面仍有优化空间。本文提出基于哈达玛卷积变换的特征提取方法，采用哈达玛矩阵的行或列向量作为卷积核，支持可变长度扩展。该方法在保持与现有方法（如ROCKET）完全兼容的同时，利用核正交性提升计算效率、鲁棒性和适应性。在多领域数据集（重点为UCR时间序列数据集）上的全面实验表明，本方法达到当前最优性能：相比ROCKET，F1分数至少提升5%，在相同超参数下训练时间比miniROCKET（最快ROCKET变体）缩短50%，可部署于超低功耗嵌入式设备。所有代码已开源。
原文摘要 · Abstract (English)
Time series classification holds broad application value in communications, information countermeasures, finance, and medicine. However, state-of-the-art (SOTA) methods-including HIVE-COTE, Proximity Forest, and TS-CHIEF-exhibit high computational complexity, coupled with lengthy parameter tuning and training cycles. In contrast, lightweight solutions like ROCKET (Random Convolutional Kernel Transform) offer greater efficiency but leave substantial room for improvement in kernel selection and computational overhead. To address these challenges, we propose a feature extraction approach based on Hadamard convolutional transform, utilizing column or row vectors of Hadamard matrices as convolution kernels with extended lengths of varying sizes. This enhancement maintains full compatibility with existing methods (e.g., ROCKET) while leveraging kernel orthogonality to boost computational efficiency, robustness, and adaptability. Comprehensive experiments on multi-domain datasets-focusing on the UCR time series dataset-demonstrate SOTA performance: F1-score improved by at least 5% vs. ROCKET, with 50% shorter training time than miniROCKET (fastest ROCKET variant) under identical hyperparameters, enabling deployment on ultra-low-power embedded devices. All code is available on GitHub.
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