arXiv:2603.15055v3 Announce Type: replace-cross Abstract: We present a theory-guided generalized Bayesian methodology for spatio-temporal raster data, which we use to train an ensemble of stochastic feed-forward neural networks with Gaussian-distributed weights. The methodology incorporates the dependence and causal structure of a spatio-temporal Ornstein-Uhlenbeck process into training and inference by enforcing constraints on the design of the data embedding and the related optimization routine. In inference mode, the networks are employed to generate causal ensemble forecasts by applying di
This paper represents continued academic progress in the application of Bayesian methodologies and neural networks for complex spatio-temporal forecasting, a field rapidly advancing due to increased data availability and computational power.
Improved spatio-temporal probabilistic forecasting is crucial for more accurate predictions in various domains, from climate modeling to resource management, impacting planning and risk assessment for strategic decision-makers.
The ability to generate causal ensemble forecasts from stochastic neural networks, especially when guided by theoretical frameworks like Ornstein-Uhlenbeck processes, enhances the reliability and interpretability of complex predictions.
- · Climate modeling and forecasting
- · Logistics and supply chain management
- · Energy grid operators
- · Quantitative finance
- · Traditional statistical forecasting methods
- · Organizations reliant on deterministic models
- · Inefficient resource allocation strategies
More accurate predictive models become available for environmental, economic, and operational systems.
Better forecasting capabilities lead to optimized resource allocation and proactive risk mitigation across industries.
Enhanced predictive intelligence could underpin new insurance products or shape policy-making in areas sensitive to spatio-temporal dynamics.
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Read at arXiv cs.LG