重新思考多变量时间序列预测中的通道依赖性：从领先指标中学习
Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators
AI总结：
针对多变量时间序列预测中通道独立方法未利用通道依赖的问题，提出即插即用的LIFT方法，通过挖掘变量间领先-滞后关系利用领先指标信息，在六个真实数据集上平均提升现有最优方法5.5%的预测性能。
摘要：
近年来，通道独立方法在多变量时间序列（MTS）预测中取得了当前最优性能。尽管这类方法降低了过拟合风险，但它们错失了利用通道依赖性实现精准预测的潜在机会。我们认为，变量之间存在局部平稳的领先-滞后关系，即部分滞后变量可能在短时间内跟随领先指标的变化。利用这种通道依赖性是有益的，因为领先指标提供的前置信息可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，该方法首先在每个时间步高效估计领先指标及其领先步长，随后审慎地让滞后变量利用来自领先指标的前置信息。LIFT是一个即插即用组件，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将当前最优方法的平均预测性能提升了5.5%。我们的代码可在https://github.com/SJTU-Quant/LIFT 获取。
英文摘要：
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. Our code is available at https://github.com/SJTU-Quant/LIFT.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
