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:本文件中的拟议方法旨在解决时间序列预测问题。虽然一些设计精美的模型取得了出色的预测业绩,但如何获取更有用的信息和作出准确的预测仍然是一个未决问题。大多数现代模型仅侧重于短范围的信息,对于诸如时间序列预测等需要捕捉长期信息特征的问题来说,这些信息是致命的。因此,这项工作的主要关切是进一步挖掘时间序列中所包含的地方和全球信息之间的关系,以得出更精确的预测。在本文件中,为了令人满意地实现目的,我们做出了三项主要贡献,即经过实验核实,从而具有业绩优势。首先,最初的时间序列转变为差异序列,作为对拟议模型的投入。第二,我们将全球的无源滑动窗口引入预报模型,其中提到模糊时间序列的概念,以便在一个时限内将相关的全球信息与时间数据联系起来,并利用中央双向直线算法来捕捉基本相关特征,以确保所采集的数据的有效性和一致性。第三,广泛使用的不对称的不对称变变式,即为半无序的竞争性序列,转换为拟议模式的投入。第二,我们把全球的滑动窗口引入了预测模型,以更灵活的方式将全球相近的纵向变换为对比的轨道。