Recently, multivariate time series (MTS) forecasting techniques have seen rapid
development and widespread applications across various fields. Transformer-based and …

Spatio-temporal prediction aims to forecast and gain insights into the ever-changing
dynamics of urban environments across both time and space. Its purpose is to anticipate …

Multivariate time series (MTS) prediction has been widely adopted in various scenarios.
Recently, some methods have employed patching to enhance local semantics and improve …

Artificial Intelligence of Things (AIoT) is an emerging frontier based on the deep fusion of
Internet of Things (IoT) and Artificial Intelligence (AI) technologies. The fundamental goal of …

With the progress of urban transportation systems, a significant amount of high-quality traffic
data is continuously collected through streaming manners, which has propelled the …

Representing information of multiple behaviors in the single graph collaborative filtering
(CF) vector has been a long-standing challenge. This is because different behaviors …

Traffic forecasting is crucial for intelligent transportation systems (ITS), aiding in efficient
resource allocation and effective traffic control. However, its effectiveness often relies heavily …

Continual learning on graph data has recently attracted paramount attention for its aim to
resolve the catastrophic forgetting problem on existing tasks while adapting the sequentially …

Time series forecasting (TSF) plays a pivotal role in many real-world applications. Recently,
the utilization of Large Language Models (LLM) in TSF has demonstrated exceptional …

Dynamic recommendation systems, where users interact with items continuously over time,
have been widely deployed in real-world online streaming applications. The burst of …