3 citations · 8 across the 19 of their papers we have counts for
10 papers · 1 filter
CTIA-ODE: Continuous-Time Inter-Variable Attention for Long-Term Time Series Forecasting
Chengyi Pan, Huangqimei Zheng, Hao Xue
Although multivariate time series forecasting has broad application prospects, effectively incorporating exogenous variables remains challenging. Prevailing discrete-time architect…
Time Series Forecasting Benchmarks Need Scenario-Grounded Stress Testing
Yuyang Zhao, Lian Xu, Hao Xue
Time series forecasting (TSF) increasingly drives decisions in transportation, energy, finance, healthcare, and infrastructure, yet current evaluation remains overly narrow: standa…
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…
Multivariate Time Series Forecasting needs Cross Variable Loss
Kuiye Ding, Yifan Hu, Hanchen Wang +1
Multivariate time series forecasting presents unique challenges because future variables often co-evolve under shared system dynamics. While existing studies mainly focus on cross-…
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…
TS-Fault: Benchmarking Time Series Forecasters Against Structural Faults
Yuyang Zhao, Lian Xu, Hao Miao +2
Time series forecasting (TSF) underpins consequential decisions in energy, transportation, finance, and healthcare, yet TSF models are almost universally ranked by a single number…