Boosting MLPs with a Coarsening Strategy for Long-Term Time Series Forecasting
<|reference_start|>Boosting MLPs with a Coarsening Strategy for Long-Term Time Series Forecasting: 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.<|reference_end|>
@article{bian2024boosting,
    title={Boosting MLPs with a Coarsening Strategy for Long-Term Time Series
  Forecasting},
    author={Nannan Bian and Minhong Zhu and Li Chen and Weiran Cai},
    journal={arXiv preprint arXiv:2405.03199},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.03199},
    primaryClass={cs.LG}
}