GenFormer: A Deep-Learning-Based Approach for Generating Multivariate Stochastic Processes
This work addresses the need for efficient stochastic generators in risk management and simulation tasks, though it appears incremental as it adapts existing Transformer methods to a specific domain.
The authors tackled the problem of generating synthetic multivariate stochastic processes that preserve target statistical properties, and they proposed GenFormer, a Transformer-based model that successfully preserves marginal distributions and approximates other statistical properties, as demonstrated in a wind speed simulation application in Florida.
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.