用原型改进随机卷积，提升时间序列分类精度与鲁棒性
SPROCKET: Extending ROCKET to Distance-Based Time-Series Transformations With Prototypes
- 基于原型选择生成卷积核，替代传统随机方式
- 在多数UCR/UEA数据集上性能媲美先进卷积模型
- 适合追求高精度与稳定性的时间序列分类研究者
经典时间序列分类方法主要依赖特征工程。其中最具代表性的是ROCKET，通过随机卷积核特征实现优异性能。本文提出SPROCKET（Selected Prototype Random Convolutional Kernel Transform），一种基于原型的新型特征工程策略。在多数UCR和UEA时间序列分类基准上，SPROCKET性能可与现有卷积算法相当；而MR-HY-SP（MultiROCKET-HYDRA-SPROCKET）集成模型平均准确率排名超越先前最优的HYDRA-MR卷积集成模型。实验表明，基于原型的特征转换能有效提升时间序列分类的准确率与鲁棒性。
原文摘要 · Abstract (English)
Classical Time Series Classification algorithms are dominated by feature engineering strategies. One of the most prominent of these transforms is ROCKET, which achieves strong performance through random kernel features. We introduce SPROCKET (Selected Prototype Random Convolutional Kernel Transform), which implements a new feature engineering strategy based on prototypes. On a majority of the UCR and UEA Time Series Classification archives, SPROCKET achieves performance comparable to existing convolutional algorithms and the new MR-HY-SP ( MultiROCKET-HYDRA-SPROCKET) ensemble's average accuracy ranking exceeds HYDRA-MR, the previous best convolutional ensemble's performance. These experimental results demonstrate that prototype-based feature transformation can enhance both accuracy and robustness in time series classification.
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