提出统一机制捕捉多变量时间序列的异步依赖关系
TiVaT: A Transformer with a Single Unified Mechanism for Capturing Asynchronous Dependencies in Multivariate Time Series Forecasting
- 用单一联合轴注意力模块同步建模时间与变量间关系
- 在复杂异步依赖场景下显著提升预测精度
- 适合处理金融、气象等存在滞后效应的数据
多变量时间序列预测在多个领域至关重要，但因需同时建模时间和变量间依赖而面临挑战。现有通道依赖模型（以Transformer为主）分别处理这些依赖，限制了对领先-滞后等复杂交互的捕捉能力。为此，我们提出TiVaT（Time-variate Transformer），引入单一统一的联合轴（JA）注意力模块，同时处理时间与变量建模。该模块动态选择相关特征，尤其擅长捕捉异步交互。此外，我们在JA注意力中引入距离感知的时间-变量采样机制，通过学习的二维嵌入空间提取显著模式，同时降低噪声。大量实验表明，TiVaT在多种数据集上表现优异，尤其在具有复杂异步依赖的场景中优势明显。
原文摘要 · Abstract (English)
Multivariate time series (MTS) forecasting is vital across various domains but remains challenging due to the need to simultaneously model temporal and inter-variate dependencies. Existing channel-dependent models, where Transformer-based models dominate, process these dependencies separately, limiting their capacity to capture complex interactions such as lead-lag dynamics. To address this issue, we propose TiVaT (Time-variate Transformer), a novel architecture incorporating a single unified module, a Joint-Axis (JA) attention module, that concurrently processes temporal and variate modeling. The JA attention module dynamically selects relevant features to particularly capture asynchronous interactions. In addition, we introduce distance-aware time-variate sampling in the JA attention, a novel mechanism that extracts significant patterns through a learned 2D embedding space while reducing noise. Extensive experiments demonstrate TiVaT's overall performance across diverse datasets, particularly excelling in scenarios with intricate asynchronous dependencies.
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