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GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable
  for Variable Missing

GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable Missing

18 May 2024
Chengqing Yu
Fei Wang
Zezhi Shao
Tangwen Qian
Zhao Zhang
Wei Wei
Yongjun Xu
    AI4CE
ArXivPDFHTML

Papers citing "GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable Missing"

3 / 3 papers shown
Title
Deep Learning for Time Series Forecasting: A Survey
X. Kong
Zhenghao Chen
Weiyao Liu
Kaili Ning
Lechao Zhang
Syauqie Muhammad Marier
Yichen Liu
Yuhao Chen
Feng Xia
AI4TS
AI4CE
48
3
0
13 Mar 2025
Exploring Progress in Multivariate Time Series Forecasting:
  Comprehensive Benchmarking and Heterogeneity Analysis
Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis
Zezhi Shao
Fei Wang
Yongjun Xu
Wei Wei
Chengqing Yu
...
Guangyin Jin
Xin Cao
Gao Cong
Christian S. Jensen
Xueqi Cheng
AI4TS
18
56
0
09 Oct 2023
Discrete Graph Structure Learning for Forecasting Multiple Time Series
Discrete Graph Structure Learning for Forecasting Multiple Time Series
Chao Shang
Jie Chen
J. Bi
CML
BDL
AI4TS
94
11
0
18 Jan 2021
1