  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.
