Stochastic generators are essential to produce synthetic realizations that preserve target statistical properties. We propose GenFormer, a stochastic generator for spatio-temporal multivariate stochastic processes. It is constructed using a Transformer-based deep learning model that learns a mapping between a Markov state sequence and time series values. The synthetic data generated by the GenFormer model preserves the target marginal distributions and approximately captures other desired statistical properties even in challenging applications involving a large number of spatial locations and a long simulation horizon. The GenFormer model is applied to simulate synthetic wind speed data at various stations in Florida to calculate exceedance probabilities for risk management.

翻译：随机生成器对于生成保留目标统计特性的合成实现至关重要。我们提出GenFormer，一种时空多变量随机过程的随机生成器。它基于Transformer深度学习模型构建，学习马尔可夫状态序列与时间序列值之间的映射关系。即使在大规模空间位置和长模拟周期等具有挑战性的应用中，GenFormer生成的合成数据也能保留目标边际分布，并近似捕捉其他所需的统计特性。该模型被应用于佛罗里达州多个站点模拟合成风速数据，以计算风险管理中的超限概率。