Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting

1 code implementation • 28 Jun 2022 • Junchen Ye, Zihan Liu, Bowen Du, Leilei Sun, Weimiao Li, Yanjie Fu, Hui Xiong

To equip the graph neural network with a flexible and practical graph structure, in this paper, we investigate how to model the evolutionary and multi-scale interactions of time series.

Multivariate Time Series Forecasting Self-Learning +1

Analysis for full face mechanical behaviors through spatial deduction model with real-time monitoring data

no code implementations • 27 Sep 2021 • Xuyan Tan, Yuhang Wang, Bowen Du, Junchen Ye, Weizhong Chen, Leilei Sun, Liping Li

Mechanical analysis for the full face of tunnel structure is crucial to maintain stability, which is a challenge in classical analytical solutions and data analysis.

Coupled Layer-wise Graph Convolution for Transportation Demand Prediction

1 code implementation • 15 Dec 2020 • Junchen Ye, Leilei Sun, Bowen Du, Yanjie Fu, Hui Xiong

Graph Convolutional Network (GCN) has been widely applied in transportation demand prediction due to its excellent ability to capture non-Euclidean spatial dependence among station-level or regional transportation demands.