Multivariate time series prediction has aroused widely research interests during decades. However, the spatial heterogeneity and temporal evolution characteristics bring much challenges for ...

Earley and his team will look to put Austin in the past as they look ahead to the No. 2 LSU Tigers in a weekend series in front of the home crowd in College Station.

Multivariate time series forecasting has extensive applications in urban computing, such as financial analysis, weather prediction, and traffic forecasting. Using graph structures to model the complex ...

Article citations More>> Li, R., Chen, Z. and Wu, W. (2000) Generalized Difference Methods for Differential Equations: Numerical Analysis of Finite Volume Methods. CRC Press. has been cited by the ...

As generative AI technologies become more mainstream, it's a good time to explore some of the other aspects that make up AI.

In this study, we propose a stochastic differential equation modeling approach to predict short-term subsidence in the temporal domain. Mining-induced time-series data collected from the Global ...

On Tuesday, MIT researchers announced that they have devised a solution to a vexing computational bottleneck, not by widening the data pipeline, but by solving a differential equation that has ...

Time series data is already ubiquitous, as it lies in every part of today’s digital businesses, but it has yet to reach its full potential within most organizations.