For many applications in econometrics, financial forecasting and climate science, data can only be obtained as aggregates. We develop and analyze a novel predictive modeling procedure for such datasets which exploits the duality properties of Fourier analysis. Empirical evaluation on three real datasets from agricultural studies, ecological surveys and climate science demonstrate the efficacy of our approach.