1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.LG2026
Dynamic Linear Coregionalization for Realistic Synthetic Multivariate Time Series
Annita Vapsi, Penghang Liu, Saheed Obitayo +8
Synthetic data is essential for training foundation models for time series (FMTS), but most generators assume static correlations, and are typically missing realistic inter-channel…
cs.AI2026★ 1 cited
TS-Agent: Understanding and Reasoning Over Raw Time Series via Iterative Insight Gathering
Penghang Liu, Elizabeth Fons, Annita Vapsi +5
Large language models (LLMs) exhibit strong symbolic and compositional reasoning, yet they struggle with time series question answering as the data is typically transformed into an…
cs.LG2025
Privacy-Aware Time Series Synthesis via Public Knowledge Distillation
Penghang Liu, Haibei Zhu, Eleonora Kreacic +1
Sharing sensitive time series data in domains such as finance, healthcare, and energy consumption, such as patient records or investment accounts, is often restricted due to privac…