Papers › Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time...
Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecasting
Zezhi Shao, Zhao Zhang, Fei Wang, Wei Wei, Yongjun Xu
Multivariate Time Series (MTS) forecasting plays a vital role in a wide range of applications. Recently, Spatial-Temporal Graph Neural Networks (STGNNs) have become increasingly popular MTS forecasting methods due to their state-of-the-art performance. However, recent works are becoming more sophisticated with limited performance improvements. This phenomenon motivates us to explore the critical factors of MTS forecasting and design a model that is as powerful as STGNNs, but more concise and efficient. In this paper, we identify the indistinguishability of samples in both spatial and temporal dimensions as a key bottleneck, and propose a simple yet effective baseline for MTS forecasting by attaching Spatial and Temporal IDentity information (STID), which achieves the best performance and efficiency simultaneously based on simple Multi-Layer Perceptrons (MLPs). These results suggest that we can design efficient and effective models as long as they solve the indistinguishability of samples, without being limited to STGNNs.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Traffic Prediction | LargeST | STID | CA MAE | 18.41 | #3 of 6 | Archive leaderboard | report |
| Traffic Prediction | LargeST | STID | GBA MAE | 20.22 | #3 of 6 | Archive leaderboard | report |
| Traffic Prediction | LargeST | STID | GLA MAE | 19.76 | #3 of 6 | Archive leaderboard | report |
| Traffic Prediction | LargeST | STID | SD MAE | 17.86 | #3 of 6 | 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.
Methods
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