Crossgnn: Confronting noisy multivariate time series via cross interaction refinement
Recently, multivariate time series (MTS) forecasting techniques have seen rapid
development and widespread applications across various fields. Transformer-based and …
development and widespread applications across various fields. Transformer-based and …
Urbangpt: Spatio-temporal large language models
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 …
dynamics of urban environments across both time and space. Its purpose is to anticipate …
Hdmixer: Hierarchical dependency with extendable patch for multivariate time series forecasting
Multivariate time series (MTS) prediction has been widely adopted in various scenarios.
Recently, some methods have employed patching to enhance local semantics and improve …
Recently, some methods have employed patching to enhance local semantics and improve …
CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Community
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 …
Internet of Things (IoT) and Artificial Intelligence (AI) technologies. The fundamental goal of …
Towards Dynamic Spatial-Temporal Graph Learning: A Decoupled Perspective
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 …
data is continuously collected through streaming manners, which has propelled the …
Multi-Behavior Collaborative Filtering with Partial Order Graph Convolutional Networks
Representing information of multiple behaviors in the single graph collaborative filtering
(CF) vector has been a long-standing challenge. This is because different behaviors …
(CF) vector has been a long-standing challenge. This is because different behaviors …
Multi-scale Traffic Pattern Bank for Cross-city Few-shot Traffic Forecasting
Traffic forecasting is crucial for intelligent transportation systems (ITS), aiding in efficient
resource allocation and effective traffic control. However, its effectiveness often relies heavily …
resource allocation and effective traffic control. However, its effectiveness often relies heavily …
Continual Learning on Graphs: Challenges, Solutions, and Opportunities
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 …
resolve the catastrophic forgetting problem on existing tasks while adapting the sequentially …
[PDF][PDF] LeRet: Language-Empowered Retentive Network for Time Series Forecasting
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 …
the utilization of Large Language Models (LLM) in TSF has demonstrated exceptional …
TCGC: Temporal Collaboration-Aware Graph Co-Evolution Learning for Dynamic Recommendation
Dynamic recommendation systems, where users interact with items continuously over time,
have been widely deployed in real-world online streaming applications. The burst of …
have been widely deployed in real-world online streaming applications. The burst of …