Higher-order Cross-structural Embedding Model for Time Series Analysis
This addresses the challenge of modeling complex interactions in time series for applications like healthcare and finance, though it appears incremental as it builds on existing Transformer and TDL methods.
The paper tackled the problem of capturing higher-order interactions in time series by proposing High-TS, a framework that jointly models temporal and spatial perspectives using multiscale Transformer and Topological Deep Learning, and it outperformed state-of-the-art methods in various tasks.
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.