Papers › Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from...
Rethinking Channel Dependence for Multivariate Time Series Forecasting: Learning from Leading Indicators
Lifan Zhao, Yanyan Shen
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.
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Syntology Ran 8 of 11 code samples harvested from 2 repositories linked to this paper; 3 have no recorded run. Of those that ran: 1 ran · honoured contract; 1 ran · violated contract; 1 ran · our draft was wrong; 5 ran with no contract checked.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Time Series Forecasting | ETTh1 (336) Multivariate | PatchTST + LIFT | MAE | 0.435 | #31 of 72 | Archive leaderboard | report |
| Time Series Forecasting | ETTh1 (336) Multivariate | PatchTST + LIFT | MSE | 0.433 | #31 of 72 | Archive leaderboard | report |
| Time Series Forecasting | ETTh1 (336) Multivariate | DLinear + LIFT | MAE | 0.453 | #39 of 72 | Archive leaderboard | report |
| Time Series Forecasting | ETTh1 (336) Multivariate | DLinear + LIFT | MSE | 0.453 | #39 of 72 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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