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.

该研究提出了一种时序预测模型，MPR-Net，通过卷积和反卷积操作，适应性地分解多尺度历史序列，并基于模式复制的先前知识构建了模式扩展预测方法，由于利用了时间依赖性，MPR-Net 不仅实现了线性时间复杂度，而且使预测过程可解释。该模型在短期和长期预测任务的十多个真实数据集上进行了充分实验，表现出最先进的预测性能以及良好的泛化性和鲁棒性能。

MPR-Net：多尺度模式再现引导的时间序列普适可解释预测

MPR-Net:Multi-Scale Pattern Reproduction Guided Universality Time Series  Interpretable Forecasting

Dunhuang murals suffer from fading, breakage, surface brittleness and
extensive peeling affected by prolonged environmental erosion. Image inpainting
techniques are widely used in the field of digital mural inpainting. Generally
speaking, for mural inpainting tasks with large area damage, it is challenging
for any image inpainting method. In this paper, we design a multi-stage
progressive reasoning network (MPR-Net) containing global to local receptive
fields for murals inpainting. This network is capable of recursively inferring
the damage boundary and progressively tightening the regional texture
constraints. Moreover, to adaptively fuse plentiful information at various
scales of murals, a multi-scale feature aggregation module (MFA) is designed to
empower the capability to select the significant features. The execution of the
model is similar to the process of a mural restorer (i.e., inpainting the
structure of the damaged mural globally first and then adding the local texture
details further). Our method has been evaluated through both qualitative and
quantitative experiments, and the results demonstrate that it outperforms
state-of-the-art image inpainting methods.

本文描述了一种基于多阶段渐进推理和多尺度特征聚合模块的数字壁画修复方法，该方法通过全局到局部感受域递归推断损伤边界和逐步加紧区域纹理约束，能有效恢复敦煌壁画。