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Yueying Wu

2 accepted papers

2025

TimeDP: Learning to Generate Multi-Domain Time Series with Domain Prompts

AAAI 2025technical

Time series generation models are crucial for applications like data augmentation and privacy preservation. Most existing time series generation models are typically designed to generate data from one specified domain. While leveraging data from other domain for better generalization is proved to wo…

Cited by 2SourcePDFScholar
2024

MG-TSD: Multi-Granularity Time Series Diffusion Models with Guided Learning Process

ICLR 2024poster

Recently, diffusion probabilistic models have attracted attention in generative time series forecasting due to their remarkable capacity to generate high-fidelity samples. However, the effective utilization of their strong modeling ability in the probabilistic time series forecasting task remains an…