Recurrent neural networks for time series forecasting: Current status and future directions
Abstract Recurrent Neural Networks (RNNs) have become competitive forecasting methods,
as most notably shown in the winning method of the recent M4 competition. However …
as most notably shown in the winning method of the recent M4 competition. However …
Deep learning for spatio-temporal data mining: A survey
With the fast development of various positioning techniques such as Global Position System
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …
Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution
Traffic prediction is the cornerstone of intelligent transportation system. Accurate traffic
forecasting is essential for the applications of smart cities, ie, intelligent traffic management …
forecasting is essential for the applications of smart cities, ie, intelligent traffic management …
Attention based spatial-temporal graph convolutional networks for traffic flow forecasting
Forecasting the traffic flows is a critical issue for researchers and practitioners in the field of
transportation. However, it is very challenging since the traffic flows usually show high …
transportation. However, it is very challenging since the traffic flows usually show high …
Spatio-temporal graph neural networks for predictive learning in urban computing: A survey
With recent advances in sensing technologies, a myriad of spatio-temporal data has been
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks
For intelligent transportation systems (ITS), predicting urban traffic crowd flows is of great
importance. However, it is challenging to represent various complex spatial relationships …
importance. However, it is challenging to represent various complex spatial relationships …
A review of irregular time series data handling with gated recurrent neural networks
Irregular time series data is becoming increasingly prevalent with the growth of multi-sensor
systems as well as the continued use of unstructured manual data recording mechanisms …
systems as well as the continued use of unstructured manual data recording mechanisms …
Attention, please! A survey of neural attention models in deep learning
A de Santana Correia, EL Colombini - Artificial Intelligence Review, 2022 - Springer
In humans, Attention is a core property of all perceptual and cognitive operations. Given our
limited ability to process competing sources, attention mechanisms select, modulate, and …
limited ability to process competing sources, attention mechanisms select, modulate, and …
Urban traffic prediction from spatio-temporal data using deep meta learning
Predicting urban traffic is of great importance to intelligent transportation systems and public
safety, yet is very challenging because of two aspects: 1) complex spatio-temporal …
safety, yet is very challenging because of two aspects: 1) complex spatio-temporal …
A data aggregation based approach to exploit dynamic spatio-temporal correlations for citywide crowd flows prediction in fog computing
Accurate and timely predicting citywide traffic crowd flows precisely is crucial for public
safety and traffic management in smart cities. Nevertheless, its crucial challenge lies in how …
safety and traffic management in smart cities. Nevertheless, its crucial challenge lies in how …