The time-series forecasting (TSF) problem is a traditional problem in the field of artificial intelligence. Models such as Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and GRU (Gate Recurrent Units) have contributed to improving the predictive accuracy of TSF. Furthermore, model structures have been proposed to combine time-series decomposition methods, such as seasonal-trend decomposition using Loess (STL) to ensure improved predictive accuracy. However, because this approach is learned in an independent model for each component, it cannot learn the relationships between time-series components. In this study, we propose a new neural architecture called a correlation recurrent unit (CRU) that can perform time series decomposition within a neural cell and learn correlations (autocorrelation and correlation) between each decomposition component. The proposed neural architecture was evaluated through comparative experiments with previous studies using five univariate time-series datasets and four multivariate time-series data. The results showed that long- and short-term predictive performance was improved by more than 10%. The experimental results show that the proposed CRU is an excellent method for TSF problems compared to other neural architectures.

翻译：时间序列预测（TSF）问题是人工智能领域的传统问题。诸如循环神经网络（RNN）、长短期记忆网络（LSTM）和门控循环单元（GRU）等模型已显著提升了TSF的预测精度。此外，研究者提出了结合时间序列分解方法（如基于Loess的季节-趋势分解（STL））的模型结构，以保证更高的预测准确性。然而，由于该方法对每个分量采用独立模型进行学习，因而无法捕捉时间序列分量之间的关系。在本研究中，我们提出了一种名为相关循环单元（CRU）的新型神经架构，该架构能够在神经单元内部实现时间序列分解，并学习各分解分量之间的相关性（自相关与互相关）。通过使用五个单变量时间序列数据集和四个多变量时间序列数据，将该神经架构与以往研究进行了对比实验。结果表明，长短期预测性能提升了10%以上。实验证明，与其他神经架构相比，所提出的CRU在TSF问题上是一种卓越方法。