Recently, channel-independent methods have achieved state-of-the-art performance in multivariate time series (MTS) forecasting. Despite reducing overfitting risks, these methods miss potential opportunities in utilizing channel dependence for accurate predictions. We argue that there exist locally stationary lead-lag relationships between variates, i.e., some lagged variates may follow the leading indicators within a short time period. Exploiting such channel dependence is beneficial since leading indicators offer advance information that can be used to reduce the forecasting difficulty of the lagged variates. In this paper, we propose a new method named LIFT that first efficiently estimates leading indicators and their leading steps at each time step and then judiciously allows the lagged variates to utilize the advance information from leading indicators. LIFT plays as a plugin that can be seamlessly collaborated with arbitrary time series forecasting methods. Extensive experiments on six real-world datasets demonstrate that LIFT improves the state-of-the-art methods by 5.5% in average forecasting performance.

翻译：近期，基于通道独立的方法在多元时间序列预测中取得了最先进的性能。尽管这些方法降低了过拟合风险，但它们忽略了利用通道依赖性实现准确预测的潜在机会。我们认为变量间存在局部平稳的领先-滞后关系，即在短时间内某些滞后变量可能跟随领先指标。利用这种通道依赖性是有益的，因为领先指标能提供超前信息，从而降低滞后变量的预测难度。本文提出了一种名为LIFT的新方法，该方法首先高效估计每个时间步的领先指标及其领先步长，然后明智地允许滞后变量利用领先指标的超前信息。LIFT作为一种插件，可与任意时间序列预测方法无缝协作。在六个真实世界数据集上的大量实验表明，LIFT将最先进方法的平均预测性能提升了5.5%。