Mixed moving average field guided learning for spatio-temporal data

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• Imma Valentina Curato (Chemnitz University of Technology)
• Tuesday 03 June 2025, 11:45-12:05
• Seminar Room 1, Newton Institute.

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RCLW02 - Calibrating prediction uncertainty : statistics and machine learning perspectives

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.    

This talk is part of the Isaac Newton Institute Seminar Series series.

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