joint work with Lorenzo Proietti (TU Chemnitz)
Influenced mixed moving average fields (MMAF) are a versatile modeling class for spatio-temporal data. However, their predictive distribution is not generally known. Under this modeling assumption, we define a novel spatio-temporal embedding and a theory-guided machine learning approach that employs a generalized Bayesian algorithm to make ensemble forecasts. Performing causal forecast is a highlight of our methodology as its potential application to data with temporal and spatial short and long-range dependence.