Early Time Series Classification (ETSC) is critical in time-sensitive medical applications such as sepsis, yet it presents an inherent trade-off between accuracy and earliness. This trade-off arises from two core challenges: 1) models should effectively model inherently weak and noisy early-stage snippets, and 2) they should resolve the complex, dual requirement of simultaneously capturing local, subject-specific variations and overarching global temporal patterns. Existing methods struggle to overcome these underlying challenges, often forcing a severe compromise: sacrificing accuracy to achieve earliness, or vice-versa. We propose \textbf{DE3S}, a \textbf{D}ual-\textbf{E}nhanced \textbf{S}oft-\textbf{S}parse \textbf{S}equence Learning framework, which systematically solves these challenges. A dual enhancement mechanism is proposed to enhance the modeling of weak, early signals. Then, an attention-based patch module is introduced to preserve discriminative information while reducing noise and complexity. A dual-path fusion architecture is designed, using a sparse mixture of experts to model local, subject-specific variations. A multi-scale inception module is also employed to capture global dependencies. Experiments on six real-world medical datasets show the competitive performance of DE3S, particularly in early prediction windows. Ablation studies confirm the effectiveness of each component in addressing its targeted challenge. The source code is available \href{https://github.com/kuxit/DE3S}{\textbf{here}}.

翻译：早期时间序列分类（ETSC）在脓毒症等时间敏感的医疗应用中至关重要，但它在准确性与早期性之间存在着固有的权衡。这一权衡源于两个核心挑战：1）模型应有效建模本质上微弱且噪声严重的早期片段；2）模型需同时满足捕获局部、个体特异性变异与整体全局时间模式的复杂双重需求。现有方法难以克服这些根本挑战，通常被迫做出严重妥协：为达成早期性而牺牲准确性，或反之。我们提出 \textbf{DE3S}，一种 \textbf{双重增强软稀疏序列学习框架}，系统性地解决了这些挑战。该框架提出了一种双重增强机制，以强化对微弱早期信号的建模。随后，引入基于注意力的补丁模块，在降低噪声和复杂度的同时保留判别性信息。设计了一种双路径融合架构，利用专家稀疏混合来建模局部、个体特异性变异。同时采用多尺度初始模块以捕获全局依赖关系。在六个真实世界医疗数据集上的实验表明，DE3S 具有竞争性的性能，尤其在早期预测窗口表现突出。消融研究证实了每个组件在应对其目标挑战方面的有效性。源代码可在 \href{https://github.com/kuxit/DE3S}{\textbf{此处}} 获取。