SurF: A Generative Model for Multivariate Irregular Time Series Forecasting

• URL: http://arxiv.org/abs/2605.14069v1
• Date: Wed, 13 May 2026 19:46:48 GMT
• Title: SurF: A Generative Model for Multivariate Irregular Time Series Forecasting
• Abstract summary: We propose SurF, a generative model that learns between event sequences and i.i.d. unit-rate exponential noise.<n>On six real-world benchmarks, SurF achieves the best reported time RMSE on Earthquake, Retweet, and Taobao.<n>Under a strict leave-one-out protocol, SurF beats every classical and neural-autoregressive baseline on 5/6 datasets.
• Score: 9.605729240308543
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Irregularly sampled multivariate event streams remain a stubbornly difficult modality for generative modeling: tokenization-based approaches break down when inter-event intervals vary by orders of magnitude, and neural temporal point processes are bottlenecked by window-level numerical quadrature. We (i) propose SurF, a generative model that uses the Time Rescaling Theorem (TRT) as a learnable bijection between event sequences and i.i.d.\ unit-rate exponential noise, enabling a single model to be trained across heterogeneous event-stream datasets; (ii) three efficient parameterizations of the cumulative intensity that scale to long sequences; and (iii) a Transformer-based encoder for multi-dataset pretraining. On six real-world benchmarks, SurF achieves the best reported time RMSE on Earthquake, Retweet, and Taobao, and is within trial-level noise of the strongest specialist on the remaining three. Under a strict leave-one-out protocol, the held-out checkpoint beats every classical and neural-autoregressive baseline on 5/6 datasets and beats every baseline on Amazon and Earthquake, an initial step toward foundation models over asynchronous event streams.

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