3 papers
cs.LG2026
StaTS: Spectral Trajectory Schedule Learning for Adaptive Time Series Forecasting with Frequency Guided Denoiser
Jintao Zhang, Zirui Liu, Mingyue Cheng +3
Diffusion models have been used for probabilistic time series forecasting and show strong potential. However, fixed noise schedules often produce intermediate states that are hard…
cs.LG2025
Bridging the Last Mile of Prediction: Enhancing Time Series Forecasting with Conditional Guided Flow Matching
Huibo Xu, Runlong Yu, Likang Wu +2
Existing generative models for time series forecasting often transform simple priors (typically Gaussian) into complex data distributions. However, their sampling initialization, i…
cs.LG2025
NeuTSFlow: Modeling Continuous Functions Behind Time Series Forecasting
Huibo Xu, Likang Wu, Xianquan Wang +4
Time series forecasting is a fundamental task with broad applications, yet conventional methods often treat data as discrete sequences, overlooking their origin as noisy samples of…