TL; DR:We introduce EAC, which follows the two fundamental tuning principle and learns prompt parameters pool only through expand and compress, simply, effectively and efficiently solving the continual spatio-temporal graph forecasting problem. EAC 4 Learning Spatiotemporal Dynamical Systems from Point P...
6. A Generative Pre-Training Framework for Spatio-Temporal Graph Transfer Learning 7. AirPhyNet: Harnessing Physics-Guided Neural Networks for Air Quality Prediction 8. Bayesian Optimization through Gaussian Cox Process Models for Spatio-temporal Data 相关链接 ICLR(International Conference on Learning Rep...
Spatiotemporal Applications 1. Transportation Marketplace Rate Forecast Using Signature Transform 2. MARLP: Time-series Forecasting Control for Agricultural Managed Aquifer Recharge 3. Diffusion Model-based Mobile Traffic Generation with Open Data for Network Planning and Optimization 4. LaDe: The First ...
This paper introduces a novel application of spatial-temporal graph neural networks (ST-GNNs) to predict groundwater levels. Groundwater level prediction is inherently complex, influenced by various hydrological, meteorological, and anthropogenic factors. Traditional prediction models often struggle with the...
Graph wavenet [13] merges the graph neural networks(GNN) and wavelet to solve the spatial–temporal problems. Moreover, despite the extensive applications of existing techniques [14], little research is devoted to predicting industrial energy consumption considering such complex relationships. One study...
A spatial query is received specifying a mapping function that identifies a set of temporal values for one or more objects. Geographic positions are automatically extracted from each set of temporal values for each of the one or more objects. Point objects are generated from the geographic ...
However, this task faces challenges such intraindividual action differences and long-term temporal dependencies. To address these challenges, we propose an innovative model called spatial-temporal graph neural ordinary differential equations (STG-NODE). First, in the data preprocessing stage, the dynamic...
OPTIMIZATION OF TEMPORAL AND SPATIAL DATA PROCESSING IN AN OBJECT RELATIONAL DATABASE SYSTEM not available for EP157934of corresponding document:Disclosed is a method, system, and program for processing temporal data. A spatial query is received sp... 埃德温·卡特巴,马丁·西格恩塔勒 - CN 被引量...
This study presents a procedure for the spatial–temporal clustering and optimization of aircraft descent and approach trajectories. First, the spatial–temporal similarity between two trajectories is defined. Clustering analysis are conducted to identify the prevailing trajectories. The clustering centers obt...
progression [170]. ST has also been applied to other neurological diseases, such as brain [171,172,173] or spinal cord [174] injury as well as neurodegenerative diseases [175,176,177]. For example, Maniatis et al. conducted a spatiotemporal profile of mouse spinal cord over the course of...