计算机科学
图形
空间分析
构造(python库)
空间相关性
多元统计
模式识别(心理学)
时间序列
人工智能
邻接表
数据挖掘
算法
理论计算机科学
机器学习
数学
统计
电信
程序设计语言
作者
Yuteng Xiao,Kaijian Xia,H. Yin,Yudong Zhang,Zhenjiang Qian,Zhaoyang Liu,Yuehan Liang,Xiaodan Li
标识
DOI:10.1016/j.dcan.2022.06.019
摘要
The prediction for Multivariate Time Series (MTS) explores the interrelationships among variables at historical moments, extracts their relevant characteristics, and is widely used in finance, weather, complex industries and other fields. Furthermore, it is important to construct a digital twin system. However, existing methods do not take full advantage of the potential properties of variables, which results in poor predicted accuracy. In this paper, we propose the Adaptive Fused Spatial-Temporal Graph Convolutional Network (AFSTGCN). First, to address the problem of the unknown spatial-temporal structure, we construct the Adaptive Fused Spatial-Temporal Graph (AFSTG) layer. Specifically, we fuse the spatial-temporal graph based on the interrelationship of spatial graphs. Simultaneously, we construct the adaptive adjacency matrix of the spatial-temporal graph using node embedding methods. Subsequently, to overcome the insufficient extraction of disordered correlation features, we construct the Adaptive Fused Spatial-Temporal Graph Convolutional (AFSTGC) module. The module forces the reordering of disordered temporal, spatial and spatial-temporal dependencies into rule-like data. AFSTGCN dynamically and synchronously acquires potential temporal, spatial and spatial-temporal correlations, thereby fully extracting rich hierarchical feature information to enhance the predicted accuracy. Experiments on different types of MTS datasets demonstrate that the model achieves state-of-the-art single-step and multi-step performance compared with eight other deep learning models.
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