Mixed moving average field guided learning for spatio-temporal data

• URL: http://arxiv.org/abs/2301.00736v4
• Date: Fri, 2 Aug 2024 15:26:37 GMT
• Title: Mixed moving average field guided learning for spatio-temporal data
• Authors: Imma Valentina Curato, Orkun Furat, Lorenzo Proietti, Bennet Stroeh,
• Abstract summary: We define a novel Bayesian-temporal embedding and a theory-guided machine learning approach to make ensemble forecasts. We use Lipschitz predictors to determine fixed-time and any-time PAC in the batch learning setting. We then test the performance of our learning methodology by using linear predictors and data sets simulated from a dependence- Ornstein-Uhlenbeck process.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Influenced mixed moving average fields 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. We use Lipschitz predictors and determine fixed-time and any-time PAC Bayesian bounds 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. We then test the performance of our learning methodology by using linear predictors and data sets simulated from a spatio-temporal Ornstein-Uhlenbeck process.

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