ST-GIN: An Uncertainty Quantification Approach in Traffic Data Imputation with Spatio-temporal Graph Attention and Bidirectional Recurrent United Neural Networks.
Zepu WangDingyi ZhuangYankai LiJinhua ZhaoPeng SunPublished in: CoRR (2023)
Keyphrases
- spatio temporal
- neural network
- data imputation
- recurrent neural networks
- missing values
- feed forward
- missing data
- artificial neural networks
- movement patterns
- back propagation
- image sequences
- graph theory
- pattern recognition
- graph structure
- random walk
- moving objects
- neural network model
- directed graph
- spatial and temporal
- traffic flow
- weighted graph
- genetic algorithm
- space time
- image processing
- recurrent networks
- high dimensional
- multilayer perceptron
- text classification
- graph mining
- rough sets