Ngboost: Natural gradient boosting for probabilistic prediction
Abstract We present Natural Gradient Boosting (NGBoost), an algorithm for generic
probabilistic prediction via gradient boosting. Typical regression models return a point …
probabilistic prediction via gradient boosting. Typical regression models return a point …
Ensemble solar forecasting and post-processing using dropout neural network and information from neighboring satellite pixels
Ensemble weather forecasts are often found to be under-dispersed and biased. Post-
processing using spatio-temporal information is, therefore, required if one wishes to improve …
processing using spatio-temporal information is, therefore, required if one wishes to improve …
Evaluating probabilistic forecasts with scoringRules
Probabilistic forecasts in the form of probability distributions over future events have become
popular in several fields including meteorology, hydrology, economics, and demography. In …
popular in several fields including meteorology, hydrology, economics, and demography. In …
Efficient seismic fragility analysis method utilizing ground motion clustering and probabilistic machine learning
Abstract Machine learning (ML) techniques have been recently adopted in engineering
practice to define the relationship between seismic intensity measure (IM) and structural …
practice to define the relationship between seismic intensity measure (IM) and structural …
Prediction skill of extended range 2-m maximum air temperature probabilistic forecasts using machine learning post-processing methods
T Peng, X Zhi, Y Ji, L Ji, Y Tian - Atmosphere, 2020 - mdpi.com
The extended range temperature prediction is of great importance for public health, energy
and agriculture. The two machine learning methods, namely, the neural networks and …
and agriculture. The two machine learning methods, namely, the neural networks and …
Countdown regression: sharp and calibrated survival predictions
Probabilistic survival predictions (ie personalized survival curves) from models trained with
Maximum Likelihood Estimation (MLE) can have high, and sometimes unacceptably high …
Maximum Likelihood Estimation (MLE) can have high, and sometimes unacceptably high …
Runoff probability prediction model based on natural Gradient boosting with tree-structured parzen estimator optimization
K Shen, H Qin, J Zhou, G Liu - Water, 2022 - mdpi.com
Accurate and reliable runoff prediction is critical for solving problems related to water
resource planning and management. Deterministic runoff prediction methods cannot meet …
resource planning and management. Deterministic runoff prediction methods cannot meet …
Remember the past: a comparison of time-adaptive training schemes for non-homogeneous regression
Non-homogeneous regression is a frequently used post-processing method for increasing
the predictive skill of probabilistic ensemble weather forecasts. To adjust for seasonally …
the predictive skill of probabilistic ensemble weather forecasts. To adjust for seasonally …
Day-ahead parametric probabilistic forecasting of wind and solar power generation using bounded probability distributions and hybrid neural networks
The penetration of renewable energy sources in modern power systems increases at an
impressive rate. Due to their intermittent and uncertain nature, it is important to forecast their …
impressive rate. Due to their intermittent and uncertain nature, it is important to forecast their …
D‐vine‐copula‐based postprocessing of wind speed ensemble forecasts
Current practice in predicting future weather is the use of numerical weather prediction
(NWP) models to produce ensemble forecasts. Despite of enormous improvements over the …
(NWP) models to produce ensemble forecasts. Despite of enormous improvements over the …