GANNSTER: Graph-Augmented Neural Network Spatio-Temporal Reasoner for Traffic Forecasting.
Carlos Salort SánchezAlexander WiederPaolo SottoviaStefano BortoliJan BaumbachCristian AxeniePublished in: AALTD@PKDD/ECML (2020)
Keyphrases
- spatio temporal
- neural network
- prediction model
- movement patterns
- spatial and temporal
- traffic volume
- short term load forecasting
- image sequences
- neural network model
- short term
- network traffic
- graph theory
- artificial neural networks
- bp neural network
- graph representation
- spanning tree
- graph model
- weighted graph
- graph matching
- knowledge base
- moving objects
- connected components
- random walk
- graph structure
- forecasting model
- traffic flow
- back propagation
- bipartite graph
- space time
- structured data
- recurrent neural networks
- real time
- generalized regression neural network
- directed acyclic graph
- chaotic time series
- learning algorithm
- computer vision
- neural network is trained
- forecasting accuracy
- spatio temporal data
- long term
- knn
- fault diagnosis
- self organizing maps