Capturing combination patterns of long- and short-term dependencies in multivariate time series forecasting
Capturing combination patterns of long- and short-term dependencies in multivariate time series forecasting
复制标题
捕获多元时间序列预测中长期和短期依赖性的组合模式
DOI：
10.1016/j.neucom.2021.08.100
复制
发表时间：
2021
期刊：
影响因子：
6
通讯作者：
Fujimura Shigeru
中科院分区：
文献类型：
--
作者：
Song Wen;Fujimura Shigeru
多变量时间序列预测在许多领域都是一个相关且有趣的话题，包括经济学、电力消耗、太阳能和交通管理。在这些领域中，由于多变量之间的复杂依赖关系和时间维度上的混合依赖关系，对多变量时间序列进行精确预测具有挑战性。此外，大多数预测方法未能捕捉到多个变量之间不同的时间长度依赖关系的混合影响。本文提出了一种新的深度学习框架来处理这个具有挑战性的问题，称为混合依赖时间序列网络（MDTNet）。在此框架下，堆叠膨胀卷积和递归单元被用来提取多个变量之间的长期和短期混合依赖关系中的复杂模式。实验表明，我们提出的框架产生了显着的结果，优于国家的最先进的基线方法的四个基准数据集在大视野和实现竞争力的性能在短期内的所有基准数据集。
Multivariate time series forecasting has typically been a relevant and interesting topic in many fields, including economics, electricity consumption, solar energy, and traffic management. In these domains, owing to the complex dependencies among multiple variables and the mixed dependencies in the time dimension, it is challenging to forecast a multivariate time series precisely. Furthermore, most of the forecasting methods fail to capture the mixed influence of the different time-length dependencies among multiple variables. In this paper, a new deep learning framework is proposed for dealing with this challenging problem, named as mixed dependence time-series network (MDTNet). In this framework, stacked dilated convolutions and recurrent units are applied to extract the complex patterns in the long- and short-term mixed dependencies among multiple variables. The experiments show that our proposed framework yields significant results, outperforming the state-of-the-art baseline methods on three of the four benchmark datasets in large horizons and achieving a competitive performance in short horizons on all the benchmark datasets.