[2603.19970] Graph2TS: Structure-Controlled Time Series Generation via Quantile-Graph VAEs

Computer Science > Machine Learning arXiv:2603.19970 (cs) [Submitted on 20 Mar 2026] Title:Graph2TS: Structure-Controlled Time Series Generation via Quantile-Graph VAEs Authors:Shaoshuai Du, Joze M. Rozanec, Andy Pimentel, Ana-Lucia Varbanescu View a PDF of the paper titled Graph2TS: Structure-Controlled Time Series Generation via Quantile-Graph VAEs, by Shaoshuai Du and 3 other authors View PDF HTML (experimental) Abstract:Although recent generative models can produce time series with close marginal distributions, they often face a fundamental tension between preserving global temporal structure and modeling stochastic local variations, particularly for highly volatile signals with weak or irregular periodicity. Direct distribution matching in such settings can amplify noise or suppress meaningful temporal patterns. In this work, we propose a structure-residual perspective on time-series generation, viewing temporal data as the combination of a structural backbone and stochastic residual dynamics, thereby motivating the separation of global organization from sample-level variability. Based on this insight, we represent time-series structure using a quantile-based transition graph that compactly captures global distributional and temporal dependencies. Building on this representation, we propose Graph2TS, a quantile-graph conditioned variational autoencoder that performs cross-modal generation from structural graphs to time series. By conditioning generation on structure r...