Zero-Shot Time-Series Reconstruction from Statistical Summary

Retaining raw time-series data is often impractical under resource constraints, leaving only coarse statistical summaries available. However, when raw observations are required for downstream analysis, reconstructing fine-grained time series from summaries alone remains inherently limited. Meanwhile, deep generative and predictive models can synthesize realistic temporal patterns, but suffer from severe distribution shift when applied to unseen time series, as they are only capable of interpolating from learned data. This study proposes zero-shot time-series reconstruction from statistical summary with generative and predictive models. Statistical summary can arise from many situations where the full retention of time series is impractical, but a quick summary is feasible. We augment existing generative and predictive models with the statistical summary to enable zero-shot time-series generation. Experimental results show that the proposed method effectively reconstructs original data from statistical summary with both unseen time-range and unseen time-series generations. This allows to train a generator/predictor only once with a generic time series and use the same generator/predictor for the reconstruction of all other datasets from their statistical summaries alone.