Multivariate time series (MTS) forecasting plays a crucial role in various real-world applications, yet simultaneously capturing both temporal and inter-variable dependencies remains a challenge. Conventional Channel-Dependent (CD) models handle these dependencies separately, limiting their ability to model complex interactions such as lead-lag dynamics. To address these limitations, we propose TiVaT (Time-Variable Transformer), a novel architecture that integrates temporal and variate dependencies through its Joint-Axis (JA) attention mechanism. TiVaT's ability to capture intricate variate-temporal dependencies, including asynchronous interactions, is further enhanced by the incorporation of Distance-aware Time-Variable (DTV) Sampling, which reduces noise and improves accuracy through a learned 2D map that focuses on key interactions. TiVaT effectively models both temporal and variate dependencies, consistently delivering strong performance across diverse datasets. Notably, it excels in capturing complex patterns within multivariate time series, enabling it to surpass or remain competitive with state-of-the-art methods. This positions TiVaT as a new benchmark in MTS forecasting, particularly in handling datasets characterized by intricate and challenging dependencies.

翻译：多元时间序列预测在众多现实应用中扮演着关键角色，然而同时捕捉时间依赖性和变量间依赖性仍是一项挑战。传统的通道依赖模型分别处理这些依赖性，限制了其建模复杂交互（如领先-滞后动态）的能力。为应对这些局限，我们提出了TiVaT（时间-变量Transformer），这是一种通过其联合轴注意力机制整合时间与变量依赖性的新颖架构。TiVaT捕捉复杂变量-时间依赖性（包括异步交互）的能力，通过融入距离感知时间-变量采样得到进一步增强；该采样方法通过一个聚焦关键交互的学习型二维映射来降低噪声并提升精度。TiVaT能有效建模时间与变量依赖性，在多样化的数据集上持续展现出强劲性能。值得注意的是，它尤其擅长捕捉多元时间序列内的复杂模式，使其能够超越或与现有最先进方法保持竞争力。这使TiVaT成为多元时间序列预测领域的新基准，特别是在处理以复杂且具有挑战性的依赖性为特征的数据集方面。