CATodyNet: Cross-attention temporal dynamic graph neural network for multivariate time series classification-Reference-Cited by-同舟云学术

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CATodyNet: Cross-attention temporal dynamic graph neural network for multivariate time series classification

Published:2024-09 Issue: Volume:300 Page:112210
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Gui Haoyu^ORCID,Li Guanjun,Tang Xianghong^ORCID,Lu Jianguang

Publisher

Elsevier BV

Reference48 articles.

1. Causal inference of multivariate time series in complex industrial systems;Liang;Adv. Eng. Inform.,2024

2. Multivariate time-series prediction in industrial processes via a deep hybrid network under data uncertainty;Yao;IEEE Trans. Ind. Inform.,2022

3. Deep anomaly detection for time-series data in industrial IoT: A communication-efficient on-device federated learning approach;Liu;IEEE Internet Things J.,2020

4. An early classification approach for multivariate time series of on-vehicle sensors in transportation;Gupta;IEEE Trans. Intell. Transp. Syst.,2020

5. Multivariate time series analysis of traffic congestion measures in urban areas as they relate to socioeconomic indicators;Dadashova;Socio-Econ. Plan. Sci.,2021

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