SurF：一种用于多变量不规则时间序列生成模型
SurF: A Generative Model for Multivariate Irregular Time Series Forecasting
AI总结：
SurF提出了一种生成模型，利用时间重标定定理将事件序列转换为独立同分布的单位速率指数噪声，支持跨异构事件流数据集训练，并在六个真实世界基准上取得最佳时间RMSE。
摘要：
不规则采样多变量事件流仍然是生成模型中极具挑战性的模态：基于标记的方法在事件间隔变化数个数量级时会失效，而神经时间点过程则受限于窗口级数值积分。我们（i）提出了SurF，一种利用时间重标定定理（TRT）作为可学习双射的生成模型，使单一模型能够跨异构事件流数据集训练；（ii）三种高效的累积强度参数化，适用于长序列；（iii）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、推文和淘宝数据集上取得最佳时间RMSE，并在其余三个数据集上接近最强专家水平。在严格的留一验证协议下，所持检查点在6个数据集中的5个上优于所有经典和神经自回归基线，并在亚马逊和地震数据集上优于所有基线，这是异步事件流基础模型的初步进展。
英文摘要：
Irregularly sampled multivariate event streams remain a difficult modality for generative modeling: tokenization-based approaches break down when inter-event intervals vary by orders of magnitude. We (i) propose \textbf{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 (Code is available at https://github.com/MrRezaeiUofT/SurF).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
