Cargando…
Spatial-Temporal Convolutional Transformer Network for Multivariate Time Series Forecasting
Multivariate time series forecasting has long been a research hotspot because of its wide range of application scenarios. However, the dynamics and multiple patterns of spatiotemporal dependencies make this problem challenging. Most existing methods suffer from two major shortcomings: (1) They ignor...
Autores principales: | Huang, Lei, Mao, Feng, Zhang, Kai, Li, Zhiheng |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8838990/ https://www.ncbi.nlm.nih.gov/pubmed/35161585 http://dx.doi.org/10.3390/s22030841 |
Ejemplares similares
-
Spatial-temporal hypergraph convolutional network for traffic forecasting
por: Zhao, Zhenzhen, et al.
Publicado: (2023) -
DRCNN: decomposing residual convolutional neural networks for time series forecasting
por: Zhu, Yuzhen, et al.
Publicado: (2023) -
A Fusion Transformer for Multivariable Time Series Forecasting: The Mooney Viscosity Prediction Case
por: Yang, Ye, et al.
Publicado: (2022) -
Hierarchical attention network for multivariate time series long-term forecasting
por: Bi, Hongjing, et al.
Publicado: (2022) -
Spatiotemporal Transformer Neural Network for Time-Series Forecasting
por: You, Yujie, et al.
Publicado: (2022)