SurF用时间重标定技术统一建模异步多变量事件流，实现跨数据集通用预测。
SurF: A Generative Model for Multivariate Irregular Time Series Forecasting
- 基于时间重标定定理，将事件序列映射为独立同分布噪声，支持跨数据集训练
- 提出三种高效累积强度参数化，可处理长序列事件流
- 采用Transformer编码器实现多数据集预训练，适合异步事件流研究者
不规则采样的多变量事件流对生成建模仍具挑战：基于分词的方法在事件间隔相差数个数量级时失效，神经时间点过程则受限于窗口级数值积分。本文提出SurF，利用时间重标定定理（TRT）作为事件序列与独立同分布单位速率指数噪声之间的可学习双射，使单一模型可在异构事件流数据集上训练；提出三种可扩展至长序列的累积强度高效参数化方法；并设计基于Transformer的编码器用于多数据集预训练。在六个真实世界基准上，SurF在地震、转发和淘宝数据集上取得最优时间均方根误差，其余三组仅略逊于最强专用模型。在严格留一数据集协议下，保留检查点在5/6数据集上超越所有经典与神经自回归基线，在亚马逊与地震数据集上全面胜出，迈出构建异步事件流基础模型的第一步。
原文摘要 · Abstract (English)
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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