Machine learning techniques are now integral to the advancement of intelligent urban
services, playing a crucial role in elevating the efficiency, sustainability, and livability of …

While most time series are non-stationary, it is inevitable for models to face the distribution
shift issue in time series forecasting. Existing solutions manipulate statistical measures …

Rapid urbanization has significantly escalated traffic congestion, underscoring the need for
advanced congestion prediction services to bolster intelligent transportation systems. As one …

Accurate traffic forecasting is crucial for the development of Intelligent Transportation
Systems (ITS), playing a pivotal role in modern urban traffic management. Traditional …

Road traffic forecasting is crucial in real-world intelligent transportation scenarios like traffic
dispatching and path planning in city management and personal traveling. Spatio-temporal …

Industrial fermentation is a crucial process for producing commonly used drugs like
penicillin. However, capturing the complex relationships among variables within a closed …

Recent advances in deep learning have led to the development of numerous models for
Long-term Time Series Forecasting (LTSF). However, most approaches still struggle to …

Urban spatio-temporal (ST) forecasting is crucial for various urban applications such as
intelligent scheduling and trip planning. Previous studies focus on modeling ST correlations …

Spatio-temporal forecasting is a critical component of various smart city applications, such
as transportation optimization, energy management, and socio-economic analysis. Recently …

Achieving fair prediction performance across nodes is crucial in the spatial-temporal
domain, as it ensures the validity and reliability of forecasting outcomes. However, existing …