[HTML][HTML] Deep learning on spatiotemporal graphs: a systematic review, methodological landscape, and research opportunities

A Zeghina, A Leborgne, F Le Ber, A Vacavant - Neurocomputing, 2024 - Elsevier
Deep learning approaches, given their low cost and high reliability, have gained much
popularity in different subjects, such as computer vision and natural language processing …

Periodic residual learning for crowd flow forecasting

C Wang, Y Liang, G Tan - … of the 30th International Conference on …, 2022 - dl.acm.org
Crowd flow forecasting, which aims to predict the crowds entering or leaving certain regions,
is a fundamental task in smart cities. One of the key properties of crowd flow data is …

A Comprehensive Survey on Automated Machine Learning for Recommendations

B Chen, X Zhao, Y Wang, W Fan, H Guo… - ACM Transactions on …, 2023 - dl.acm.org
Deep recommender systems (DRS) are critical for current commercial online service
providers, which address the issue of information overload by recommending items that are …

A feature extraction and deep learning approach for network traffic volume prediction considering detector reliability

X Zou, E Chung, Y Zhou, M Long… - Computer‐Aided Civil …, 2024 - Wiley Online Library
Accurate traffic volume prediction plays a crucial role in urban traffic control by relieving
congestion through improved regulation of traffic volume. Network‐level traffic volume …

Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting

X Wu, D Zhang, M Zhang, C Guo, B Yang… - arXiv preprint arXiv …, 2022 - arxiv.org
Sensors in cyber-physical systems often capture interconnected processes and thus emit
correlated time series (CTS), the forecasting of which enables important applications. The …

CityCAN: Causal Attention Network for Citywide Spatio-Temporal Forecasting

C Wang, Y Liang, G Tan - Proceedings of the 17th ACM International …, 2024 - dl.acm.org
Citywide spatio-temporal (ST) forecasting is a fundamental task for many urban applications,
including traffic accident prediction, taxi demand planning, and crowd flow forecasting. The …

Physics-coupled spatio-temporal active learning for dynamical systems

Y Huang, Y Tang, X Zhu, H Zhuang, L Cherubin - IEEE Access, 2022 - ieeexplore.ieee.org
Spatio-temporal forecasting is of great importance in a wide range of dynamic systems
applications, such as earth science, transport planning, etc. These applications rely on …

MFNet: The spatio-temporal network for meteorological forecasting with architecture search

X Zhang, Q Jin, S Xiang, C Pan - IEEE Geoscience and Remote …, 2022 - ieeexplore.ieee.org
Exploiting deep learning for the meteorological forecasting (MF) task is challenging due to
the complex spatio-temporal correlation, non-stationarity, and imbalanced data distribution …

Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting

J Deng, X Chen, R Jiang, D Yin, Y Yang… - … on Knowledge and …, 2024 - ieeexplore.ieee.org
Multivariate time-series (MTS) forecasting is a paramount and fundamental problem in many
real-world applications. The core issue in MTS forecasting is how to effectively model …

Heterogeneous Modular Traffic Prediction Based on Multilayer Graph Convolutional Network

M Chang, Z Ding, Z Zhao, Z Cai - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Traffic patterns in the spatiotemporal network are affected by temporal dynamics and spatial
correlations. The network flows have different strengths interacting at various implicit layers …