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The paper introduces U-Former ODE (UFO), a novel architecture for probabilistic forecasting of irregular time series data that combines U-Nets, Transformers, and Neural CDEs. UFO enables parallelizable computation and global receptive fields, addressing the scalability limitations of existing Neural CDE approaches. Experiments on five benchmarks demonstrate that UFO outperforms ten state-of-the-art baselines in predictive accuracy and achieves up to 15x faster inference, particularly on long and multivariate sequences.
Achieve 15x faster inference for probabilistic forecasting of irregular time series by fusing U-Nets, Transformers, and Neural CDEs into a parallelizable architecture.
Probabilistic forecasting of irregularly sampled time series is crucial in domains such as healthcare and finance, yet it remains a formidable challenge. Existing Neural Controlled Differential Equation (Neural CDE) approaches, while effective at modelling continuous dynamics, suffer from slow, inherently sequential computation, which restricts scalability and limits access to global context. We introduce UFO (U-Former ODE), a novel architecture that seamlessly integrates the parallelizable, multiscale feature extraction of U-Nets, the powerful global modelling of Transformers, and the continuous-time dynamics of Neural CDEs. By constructing a fully causal, parallelizable model, UFO achieves a global receptive field while retaining strong sensitivity to local temporal dynamics. Extensive experiments on five standard benchmarks -- covering both regularly and irregularly sampled time series -- demonstrate that UFO consistently outperforms ten state-of-the-art neural baselines in predictive accuracy. Moreover, UFO delivers up to 15$\times$ faster inference compared to conventional Neural CDEs, with consistently strong performance on long and highly multivariate sequences.
