14 citations · 24 across the 16 of their papers we have counts for
9 papers · 1 filter
Bus-Conditioned Zero-Shot Trajectory Generation via Task Arithmetic
Shuai Liu, Ning Cao, Yile Chen +2
Mobility trajectory data provide essential support for smart city applications. However, such data are often difficult to obtain. Meanwhile, most existing trajectory generation met…
STRATA-TS: Selective Knowledge Transfer for Urban Time Series Forecasting with Retrieval-Guided Reasoning
Yue Jiang, Chenxi Liu, Yile Chen +4
Urban forecasting models often face a severe data imbalance problem: only a few cities have dense, long-span records, while many others expose short or incomplete histories. Direct…
FlexTSF: A Flexible Forecasting Model for Time Series with Variable Regularities
Jingge Xiao, Yile Chen, Gao Cong +2
Forecasting time series with irregular temporal structures remains challenging for universal pre-trained models. Existing approaches often assume regular sampling or depend heavily…
NextLocLLM: Location Semantics Modeling and Coordinate-Based Next Location Prediction with LLMs
Shuai Liu, Ning Cao, Yile Chen +3
Next location prediction is a critical task in human mobility analysis.Existing methods typically formulate it as a classification task based on discrete location IDs, which hinder…
UrbanLLM: Autonomous Urban Activity Planning and Management with Large Language Models
Yue Jiang, Qin Chao, Yile Chen +3
Location-based services play an critical role in improving the quality of our daily lives. Despite the proliferation of numerous specialized AI models within spatio-temporal contex…
SAGDFN: A Scalable Adaptive Graph Diffusion Forecasting Network for Multivariate Time Series Forecasting
Yue Jiang, Xiucheng Li, Yile Chen +4
Time series forecasting is essential for our daily activities and precise modeling of the complex correlations and shared patterns among multiple time series is essential for impro…
Road Network Representation Learning with the Third Law of Geography
Haicang Zhou, Weiming Huang, Yile Chen +3
Road network representation learning aims to learn compressed and effective vectorized representations for road segments that are applicable to numerous tasks. In this paper, we id…
Semantic-Enhanced Representation Learning for Road Networks with Temporal Dynamics
Yile Chen, Xiucheng Li, Gao Cong +2
In this study, we introduce a novel framework called Toast for learning general-purpose representations of road networks, along with its advanced counterpart DyToast, designed to e…
From Chaos to Clarity: Time Series Anomaly Detection in Astronomical Observations
Xinli Hao, Yile Chen, Chen Yang +4
With the development of astronomical facilities, large-scale time series data observed by these facilities is being collected. Analyzing anomalies in these astronomical observation…