9 papers
A2TTA: Anchored-and-Agile Test-Time Adaptation for Evolving Traffic Sensor Networks
Du Yin, Xiachong Lin, Yue Tan +4
Traffic forecasting is important for efficient traffic management and route planning in smart cities. Existing traffic forecasting studies typically assume fixed sensor graphs, ove…
StrideDiffusion: Accelerating Diffusion Models for Time-series Generation
Du Yin, Estrid He, Julián Jerónimo Bañuelos +6
Diffusion models have become competitive generators for time series, but their practical use is limited by the large number of sequential denoising steps required at inference time…
TopoBrick: Agentic Topology Sampling of Exogenous Variables for Zero-Shot Building IoT Forecasting
Xiachong Lin, Du Yin, Arian Prabowo +6
Building sensors are embedded in physical topology, spatial hierarchy, and operational context, yet existing forecasters often treat them as isolated time series or rely on fixed c…
UPLOTS: A Unified Pretrained Language Model for Constrained Time-series Generation
Du Yin, Hao Xue, Jinliang Deng +4
In time-series generation, existing approaches typically handcraft ortrain a separate model for each dataset, which hinders their scalability and fails to leverage shared temporal…
From XXLTraffic to EvoXXLTraffic: Scaling Traffic Forecasting to Sensor-Evolving Networks
Du Yin, Hao Xue, Arian Prabowo +2
Existing traffic forecasting benchmarks assume a fixed sensor set, but real road-sensor networks grow continuously as the road network changes year by year. We introduce the XXLTra…
DeepLévy: Learning Heavy-Tailed Uncertainty in Highly Volatile Time Series
Yang Yang, Du Yin, Hao Xue +1
Modeling uncertainty in heavy-tailed time series remains a critical challenge for deep probabilistic forecasting models, which often struggle to capture abrupt, extreme events. Whi…