Author

Milad Abdollahzadeh

2 papers indexed here

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Preprint Oct 2026

FracTS: Hierarchical and Autoregressive Time Series Generation

Time series are ubiquitous across different domains, and the growing use of synthetic data for privacy-preserving sharing, data augmentation, and stress testing makes time series generation increasingly important. Early generative approaches are predominantly autoregressive, which can suffer from low sample diversity,...

Jia-Yu Li, Umair Afzal, Zi-Long Zhao et al. · 0 citations

#machine learning Preprint Sep 2026

Synthesis Without Training: An Inference-Only Pipeline for Tabular, Temporal, and Relational Synthetic Data

Synthetic data generation is dominated by the fit-then-sample paradigm: a generative model is trained on a private dataset and then sampled from. Despite its widespread adoption, this paradigm faces three challenges: (1) a new training run is required for every dataset; (2) different data modalities, such as single tab...

Zi-Long Zhao, Abdul Raheem, Jia-Yu Li et al. · 0 citations