Mixed moving average field guided learning for raster data cubes

Curato Imma, TU Chemnitz

Influenced mixed moving average fields are a versatile modeling class for raster data cubes. 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. We employ Lipschitz predictors and determine an any-time PAC Bayesian bound in the batch learning setting. Performing causal forecast is a highlight of our methodology as its potential application to data with spatial and temporal short and long-range dependence.

Area: CS22 - Statistics for Stochastic Processes and applications (Chiara Amorino)

Keywords: stationary models, weak dependence, randomized estimators, ensemble forecast, causal forecasts

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