We first categorize the spatio-temporal data into five different types, and then briefly introduce the deep learning models that are widely used in STDM. Next, we classify existing literature based on the types
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...
Deep Learning for Spatio-Temporal Data Mining: A Survey 2022, IEEE Transactions on Knowledge and Data Engineering Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions 2021, IEEE Transactions on Intelligent Transportation Systems Deep Learning for Time Series Forecasting: A ...
The complementary strengths and challenges between spatiotemporal data computing and deep learning in recent years suggest urgent needs to bring together the experts in these two domains in prestigious venues, which is still missing until now. This workshop will provide a premium platform for both r...
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...
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...
deep learning methods can help address data scarcity by filling temporal and spatial gaps and aid in formulating and testing hypotheses via identifying influential drivers of water quality. This Review highlights the strengths and limitations of deep learning methods relative to traditional approaches, ...
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 ...
Data mining and analysis are critical for preventing or mitigating natural hazards. However, data availability in natural hazard analysis is experiencing unprecedented challenges due to economic, technical, and environmental constraints. Recently, generative deep learning has become an increasingly attractive ...
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...