The proposed method in this paper is designed to address the problem of time series forecasting. Although some exquisitely designed models achieve excellent prediction performances, how to extract more useful information and make accurate predictions is still an open issue. Most of modern models only focus on a short range of information, which are fatal for problems such as time series forecasting which needs to capture long-term information characteristics. As a result, the main concern of this work is to further mine relationship between local and global information contained in time series to produce more precise predictions. In this paper, to satisfactorily realize the purpose, we make three main contributions that are experimentally verified to have performance advantages. Firstly, original time series is transformed into difference sequence which serves as input to the proposed model. And secondly, we introduce the global atrous sliding window into the forecasting model which references the concept of fuzzy time series to associate relevant global information with temporal data within a time period and utilizes central-bidirectional atrous algorithm to capture underlying-related features to ensure validity and consistency of captured data. Thirdly, a variation of widely-used asymmetric convolution which is called semi-asymmetric convolution is devised to more flexibly extract relationships in adjacent elements and corresponding associated global features with adjustable ranges of convolution on vertical and horizontal directions. The proposed model in this paper achieves state-of-the-art on most of time series datasets provided compared with competitive modern models.

翻译：本文提出的方法旨在解决时间序列预测问题。尽管某些精巧设计的模型已取得优异的预测性能，但如何提取更多有用信息并实现精准预测仍是一个开放性难题。现有大多数模型仅关注短程信息，这对需要捕捉长期信息特征的时间序列预测等问题而言是致命缺陷。因此，本工作的核心在于进一步挖掘时间序列中局部与全局信息之间的关联，以生成更精确的预测结果。为实现此目标，本文提出三项经实验验证具有性能优势的主要贡献。首先，将原始时间序列转换为差分序列作为模型输入；其次，引入全局空洞滑动窗口至预测模型，该窗口借鉴模糊时间序列概念，将时段内相关全局信息与时间数据相关联，并采用中心双向空洞算法捕捉潜在关联特征，以确保所捕获数据的有效性与一致性；最后，设计一种名为半非对称卷积的变体结构，在水平和垂直方向的可调整卷积范围内，更灵活地提取相邻元素间的关联关系及对应的全局特征。与当前先进对比模型相比，本文提出的模型在大多数时间序列数据集上达到了最优性能。