Boosting MLPs with a Coarsening Strategy for Long-Term Time Series Forecasting

• URL: http://arxiv.org/abs/2405.03199v2
• Date: Mon, 20 May 2024 07:48:21 GMT
• Title: Boosting MLPs with a Coarsening Strategy for Long-Term Time Series Forecasting
• Authors: Nannan Bian, Minhong Zhu, Li Chen, Weiran Cai,
• Abstract summary: Deep learning methods have been exerting their strengths in long-term time series forecasting. They often struggle to strike a balance between expressive power and computational efficiency. Here, we propose a coarsening strategy that alleviates the problems associated with the prototypes by forming information granules in place of solitary temporal points. Based purely on convolutions of structural simplicity, CP-Net is able to maintain a linear computational complexity and low runtime, while demonstrating an improvement of 4.1% compared with the SOTA method on seven forecasting benchmarks.
• Score: 6.481470306093991
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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