Mixed moving average field guided learning for spatio-temporal data. (arXiv:2301.00736v2 [stat.ML] UPDATED)

Influenced mixed moving average fields are a versatile modeling class for
spatio-temporal data. However, their predictive distribution is not generally
accessible. Under this modeling assumption, we define a novel theory-guided
machine learning approach that employs a generalized Bayesian algorithm to make
predictions. We employ a Lipschitz predictor, for example, a linear model or a
feed-forward neural network, and determine a randomized estimator by minimizing
a novel PAC Bayesian bound for data serially correlated along a spatial and
temporal dimension. Performing …

algorithm arxiv bayesian data distribution example fields future generalized highlight linear linear model machine machine learning mixed modeling moving network neural network novel predictions predictive temporal theory