同时捕捉时间与空间复杂关系，提升时序分析效果
Higher-order Cross-structural Embedding Model for Time Series Analysis
- 用多尺度Transformer结合拓扑深度学习，联合建模时序与空间结构
- 在多个时序任务上超越现有方法，证明高阶交叉结构信息重要
- 适合需要精准建模复杂时序依赖的研究者和工业应用
时序分析因在医疗、金融、传感器网络等领域的关键应用而备受关注。时序数据的复杂性和非平稳性使得跨时间戳交互模式难以捕捉。当前方法通常仅单独学习时间或空间依赖，难以建模时序中的高阶交互，限制了下游任务性能。为此，我们提出面向时序分析的高阶交叉结构嵌入模型（High-TS），通过结合多尺度Transformer与拓扑深度学习（TDL），联合建模时间与空间视角。同时，High-TS利用对比学习融合两种结构，生成鲁棒且判别性强的表示。大量实验表明，High-TS在多种时序任务中优于现有最优方法，验证了高阶交叉结构信息对提升模型性能的关键作用。
原文摘要 · Abstract (English)
Time series analysis has gained significant attention due to its critical applications in diverse fields such as healthcare, finance, and sensor networks. The complexity and non-stationarity of time series make it challenging to capture the interaction patterns across different timestamps. Current approaches struggle to model higher-order interactions within time series, and focus on learning temporal or spatial dependencies separately, which limits performance in downstream tasks. To address these gaps, we propose Higher-order Cross-structural Embedding Model for Time Series (High-TS), a novel framework that jointly models both temporal and spatial perspectives by combining multiscale Transformer with Topological Deep Learning (TDL). Meanwhile, High-TS utilizes contrastive learning to integrate these two structures for generating robust and discriminative representations. Extensive experiments show that High-TS outperforms state-of-the-art methods in various time series tasks and demonstrate the importance of higher-order cross-structural information in improving model performance.
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