Papers › Boosting MLPs with a Coarsening Strategy for Long-Term Time Series Forecasting
Boosting MLPs with a Coarsening Strategy for Long-Term Time Series Forecasting
Nannan Bian, Minhong Zhu, Li Chen, Weiran Cai
Deep learning methods have been exerting their strengths in long-term time series forecasting. However, they often struggle to strike a balance between expressive power and computational efficiency. Resorting to multi-layer perceptrons (MLPs) provides a compromising solution, yet they suffer from two critical problems caused by the intrinsic point-wise mapping mode, in terms of deficient contextual dependencies and inadequate information bottleneck. Here, we propose the Coarsened Perceptron Network (CP-Net), featured by a coarsening strategy that alleviates the above problems associated with the prototype MLPs by forming information granules in place of solitary temporal points. The CP-Net utilizes primarily a two-stage framework for extracting semantic and contextual patterns, which preserves correlations over larger timespans and filters out volatile noises. This is further enhanced by a multi-scale setting, where patterns of diverse granularities are fused towards a comprehensive prediction. Based purely on convolutions of structural simplicity, CP-Net is able to maintain a linear computational complexity and low runtime, while demonstrates an improvement of 4.1% compared with the SOTA method on seven forecasting benchmarks.
Code
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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 | CPNet | MAE | 0.450 | #57 of 72 | Archive leaderboard | report |
| Time Series Forecasting | ETTh1 (336) Multivariate | CPNet | MSE | 0.479 | #57 of 72 | Archive leaderboard | report |
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