用动态模态分解自动提取时间模式，提升长时序预测精度。
Dynamic Modes as Time Representation for Spatiotemporal Forecasting
- 通过DMD从数据中直接提取多尺度时间模式，无需人工设计时间特征。
- 在交通、气候等数据上，显著提升长期预测准确率并降低残差相关性。
- 轻量且通用，可无缝接入任意带时间变量的时空模型。
本文提出一种数据驱动的时间嵌入方法，用于建模时空预测中的长程季节依赖关系。该方法利用动态模态分解（DMD）直接从观测数据中提取时间模式，无需显式时间戳或手工设计的时间特征。这些时间模式作为时间表示，可无缝集成到深度时空预测模型中。与传统的时间嵌入（如时段指示或正弦函数）相比，本方法通过时空数据的谱分析捕捉复杂多尺度周期性。在城市出行、高速公路交通和气候数据集上的大量实验表明，基于DMD的嵌入方法能持续提升长期预测精度，降低残差相关性，并增强时间泛化能力。该方法轻量、模型无关，兼容任何包含时间协变量的架构。
原文摘要 · Abstract (English)
This paper introduces a data-driven time embedding method for modeling long-range seasonal dependencies in spatiotemporal forecasting tasks. The proposed approach employs Dynamic Mode Decomposition (DMD) to extract temporal modes directly from observed data, eliminating the need for explicit timestamps or hand-crafted time features. These temporal modes serve as time representations that can be seamlessly integrated into deep spatiotemporal forecasting models. Unlike conventional embeddings such as time-of-day indicators or sinusoidal functions, our method captures complex multi-scale periodicity through spectral analysis of spatiotemporal data. Extensive experiments on urban mobility, highway traffic, and climate datasets demonstrate that the DMD-based embedding consistently improves long-horizon forecasting accuracy, reduces residual correlation, and enhances temporal generalization. The method is lightweight, model-agnostic, and compatible with any architecture that incorporates time covariates.
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