7 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…
AsyTO: Asymmetric Temporal Operator for Parameter-Efficient Multivariate Time Series Forecasting
Xiachong Lin, Du Yin, Hao Xue +5
Multivariate time-series forecasting faces a structural dilemma: sharing one temporal predictor across variables is parameter-efficient but forces heterogeneous variables through a…
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…
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…
STOAT: Spatial-Temporal Probabilistic Causal Inference Network
Yang Yang, Du Yin, Hao Xue +1
Spatial-temporal causal time series (STC-TS) involve region-specific temporal observations driven by causally relevant covariates and interconnected across geographic or network-ba…