Time series forecasting has received wide interest from existing research due to its broad applications and inherent challenging. The research challenge lies in identifying effective patterns in historical series and applying them to future forecasting. Advanced models based on point-wise connected MLP and Transformer architectures have strong fitting power, but their secondary computational complexity limits practicality. Additionally, those structures inherently disrupt the temporal order, reducing the information utilization and making the forecasting process uninterpretable. To solve these problems, this paper proposes a forecasting model, MPR-Net. It first adaptively decomposes multi-scale historical series patterns using convolution operation, then constructs a pattern extension forecasting method based on the prior knowledge of pattern reproduction, and finally reconstructs future patterns into future series using deconvolution operation. By leveraging the temporal dependencies present in the time series, MPR-Net not only achieves linear time complexity, but also makes the forecasting process interpretable. By carrying out sufficient experiments on more than ten real data sets of both short and long term forecasting tasks, MPR-Net achieves the state of the art forecasting performance, as well as good generalization and robustness performance.

翻译：时间序列预测因其广泛的应用和固有的挑战性而受到现有研究的广泛关注。研究难点在于识别历史序列中的有效模式，并将其应用于未来预测。基于逐点连接MLP和Transformer架构的先进模型具有强大的拟合能力，但其二次计算复杂度限制了实用性。此外，这些结构本质上会破坏时间顺序，降低信息利用率，并使预测过程不可解释。为解决这些问题，本文提出了一种预测模型MPR-Net。该模型首先利用卷积操作自适应分解多尺度历史序列模式，然后基于模式复现的先验知识构建模式扩展预测方法，最后利用反卷积操作将未来模式重建为未来序列。通过利用时间序列中存在的时间依赖性，MPR-Net不仅实现了线性时间复杂度，而且使预测过程具有可解释性。通过在十余个短期和长期预测任务的真实数据集上开展充分实验，MPR-Net达到了最先进的预测性能，同时具有良好的泛化性和稳健性。