HiGen: Hierarchical Generation of Irregular Multivariate Time Series

Irregular multivariate time series are widely observed in real-world systems, where variables are recorded asynchronously and with varying frequencies. Generating such data is useful for data augmentation, data sharing, and downstream task training. Existing methods usually regularize irregular observations into aligned sequences, discretize them into predefined fixed time bins, or model them with a single smooth continuous-time state evolution that struggles to characterize irregular sampling rhythms. These designs change the original sampling process and struggle to preserve realistic temporal dynamics and asynchronous variable interactions. We propose HiGen, a hierarchical generation framework for irregular multivariate time series. HiGen directly models the original sample-point sample and organizes generation at the sample, window, and point levels. This hierarchy is used to model temporal dynamics through real time intervals at window and point level, and to model variable interactions through sample, window, and point levels. In this way, the model preserves both irregular sampling rhythms and asynchronous variable relationships. Experiments on irregular multivariate time series datasets show that HiGen achieves better downstream utility and generation fidelity than state-of-the-art baselines.