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SUMMARY:Mixed moving average field guided learning for spatio-temporal dat
 a - Imma Valentina Curato (Chemnitz University of Technology)
DTSTART:20250603T104500Z
DTEND:20250603T110500Z
UID:TALK230797@talks.cam.ac.uk
DESCRIPTION:joint work with Lorenzo Proietti (TU Chemnitz)\nInfluenced mix
 ed moving average fields (MMAF) are a versatile modeling class for spatio-
 temporal data. However\, their predictive distribution is not generally kn
 own. Under this modeling assumption\, we define a novel spatio-temporal em
 bedding and a theory-guided machine learning approach that employs a gener
 alized Bayesian algorithm to make ensemble forecasts. Performing causal fo
 recast is a highlight of our methodology as its potential application to d
 ata with temporal and spatial short and long-range dependence.\n&nbsp\;\n&
 nbsp\;
LOCATION:Seminar Room 1\, Newton Institute
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