Title: Fast Generation, Forecasting, and Imputation of Multivariate Irregular Time Series with OUFlow
Primary area: generative models
Keywords: Generative model, Time series analysis, Deep learning, Normalizing flow, State space model

Abstract: We propose OUFlow, a novel generative model for multivariate, irregularly-sampled time series data that excels in generation, forecasting, and imputation tasks across the board. OUFlow integrates latent dynamics governed by a mixture of Ornstein-Uhlenbeck processes with expressive target distributions via normalizing flows. By exploiting parallelization through the scan algorithm, OUFlow achieves fast sampling whose runtime scales logarithmically with the number of generated points, while maintaining high accuracy in all three tasks. Comprehensive experiments on both synthetic and real-world datasets demonstrate that OUFlow consistently outperforms existing methods in terms of both generation quality and computational efficiency.