14, NO. 8, AUGUST 2015 1Deep Learning for Spatio-Temporal Data Mining:A SurveySenzhang Wang, Jiannong Cao, Fellow, IEEE, and Philip S. Yu, Fellow, IEEEAbstract—With the fast development of various positioning techniques such as Global Position System (GPS), mobile devices andremote sensing...
Deep Learning for Spatio-Temporal Data Mining: A Survey 2022, IEEE Transactions on Knowledge and Data Engineering T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction 2020, IEEE Transactions on Intelligent Transportation Systems Spatiotemporal multi-graph convolution network for ride-hailing...
Julia and Python resources on mathematical epidemiology and epidemiology informed deep learning methods. Most about package information. Main Topics include Data Preprocessing Basic Statistics and Data Visualization Differential Programing and Data Mining such as bayesian inference, deep learning, scientific...
What is the future direction for DL research in Finance? Response: Hybrid models based on Spatio-temporal data representations, NLP, semantics and text mining-based models might become more important in the near future. 7. 结语 在此调查中,我们希望重点介绍用于金融应用程序的最新DL研究。 我们不仅提...
Interpretable deep learning for spatial-temporal data. Learning representation on heterogeneous networks, knowledge graphs Deep generative models, adversarial machine learning Deep reinforcement learning Theory of deep learning for spatiotemporal data
MobilityDL: A review of deep learning from trajectory data [paper] Spatio-temporal data mining: A survey of problems and methods [paper] Deep Learning for Spatio-Temporal Data Mining: A Survey [paper] 🖲️ Taxonomy Framework This survey is structured along follow dimensions: Deep Learning for...
Deep learning (DL) is a powerful tool for mining features from data, which can theoretically avoid assumptions (e.g., linear events) constraining conventional interpolation methods. Motivated by this and inspired by image-to-image translation, we applied DL to irregularly and regularly missing data...
spatio-temporal fields measured on a set of irregular points in space is still under-investigated. To fill this gap, we introduce here a framework for spatio-temporal prediction of climate and environmental data using deep learning. Specifically, we show how spatio-temporal processes can be ...
Our approach is based on an eXplainable Deep Learning (XDL) solution15 that concurrently uses convolutional neural networks (CNN) for the prediction of river flow time series and saliency maps to explain the results by highlighting the relative importance of the spatiotemporal SST data. Our ...
deep learning (an approach that is able to extract spatio-temporal features automatically) to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales, for example...