Introduced more than a half-century ago, Granger causality has become a popular tool for
analyzing time series data in many application domains, from economics and finance to …

Domain-specific hardware is becoming a promising topic in the backdrop of improvement
slow down for general-purpose processors due to the foreseeable end of Moore's Law …

Transformers have shown great power in time series forecasting due to their global-range
modeling ability. However, their performance can degenerate terribly on non-stationary real …

Extending the forecasting time is a critical demand for real applications, such as extreme
weather early warning and long-term energy consumption planning. This paper studies the …

Many real-world applications require the prediction of long sequence time-series, such as
electricity consumption planning. Long sequence time-series forecasting (LSTF) demands a …

Time series forecasting is an important problem across many domains, including predictions
of solar plant energy output, electricity consumption, and traffic jam situation. In this paper …

This study investigates the accuracy of deep learning models for the inference of Reynolds-
averaged Navier–Stokes (RANS) solutions. This study focuses on a modernized U-net …

Accurate forecasting of citywide traffic flow has been playing critical role in a variety of
spatial-temporal mining applications, such as intelligent traffic control and public risk …

Generative modeling of multivariate time series has remained challenging partly due to the
complex, non-deterministic dynamics across long-distance timesteps. In this paper, we …

Large-scale and multidimensional spatiotemporal data sets are becoming ubiquitous in
many real-world applications such as monitoring urban traffic and air quality. Making …