  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.
