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Found 3 papers, 2 papers with code
Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization
Adversarial-free minimization is possible for some scoring rules; hence, our framework avoids the cumbersome hyperparameter tuning and uncertainty underestimation due to unstable adversarial training, thus unlocking reliable use of generative networks in probabilistic forecasting.
TRU-NET: A Deep Learning Approach to High Resolution Prediction of Rainfall
Experiments show that our model consistently attains lower RMSE and MAE scores than a DL model prevalent in short term precipitation prediction and improves upon the rainfall predictions of a state-of-the-art dynamical weather model.
Part 1: Training Sets & ASG Transforms
In this paper, I discuss a method to tackle the issues arising from the small data-sets available to data-scientists when building price predictive algorithms that use monthly/quarterly macro-financial indicators.
