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Neural-NGBoost: Natural gradient boosting with neural network base learners
ICT ExpressNGBoost has shown promising results in probabilistic and point estimation tasks. However, it is vague still whether this method can be scalable to neural architecture system since its base learner is based on decision trees.
Jamshidjon Ganiev +2 more
doaj +5 more sources
Short-Term Direct Probability Prediction Model of Wind Power Based on Improved Natural Gradient Boosting [PDF]
Energies, 2020 Wind energy has been widely used in renewable energy systems. A probabilistic prediction that can provide uncertainty information is the key to solving this problem.
Yonggang Li, Yue Wang, Binyuan Wu
doaj +4 more sources
Environmental Data Science, 2023 Many single-output regression problems require estimates of uncertainty along with the point predictions. For this purpose, there exists a class of regression algorithms that predict a conditional distribution rather than a point estimate.
Michael O’Malley +3 more
doaj +3 more sources
Multivariate Probabilistic Regression with Natural Gradient Boosting
CoRR, 2021 Many single-target regression problems require estimates of uncertainty along with the point predictions. Probabilistic regression algorithms are well-suited for these tasks. However, the options are much more limited when the prediction target is multivariate and a joint measure of uncertainty is required.
O'Malley, Michael +3 more
core +5 more sources
Runoff Probability Prediction Model Based on Natural Gradient Boosting With Tree-Structured Parzen Estimator Optimization [PDF]
Water (Switzerland), 2022 Accurate and reliable runoff prediction is critical for solving problems related to water resource planning and management. Deterministic runoff prediction methods cannot meet the needs of risk analysis and decision making. In this study, a runoff probability prediction model based on natural gradient boosting (NGboost) with tree-structured parzen ...
Jianzhong Zhou, Hui Qin, Keyan Shen
exaly +2 more sources
Atmosphere, 2020 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 natural gradient boosting (NGBoost), are applied to improve the prediction ...
Ting Peng +4 more
doaj +4 more sources
An interpretable probabilistic model for short-term solar power forecasting using natural gradient boosting [PDF]
Applied Energy, 2022 PV power forecasting models are predominantly based on machine learning algorithms which do not provide any insight into or explanation about their predictions (black boxes). Therefore, their direct implementation in environments where transparency is required, and the trust associated with their predictions may be questioned. To this end, we propose a
Georgios Mitrentsis, Hendrik Lens
openaire +2 more sources
Atmosphere, 2023 Machine learning algorithms are applied to predict intense wind shear from the Doppler LiDAR data located at the Hong Kong International Airport. Forecasting intense wind shear in the vicinity of airport runways is vital in order to make intelligent ...
Afaq Khattak +3 more
doaj +1 more source
BuildingsThe California bearing ratio (CBR) value of subgrade is the most used parameter for dimensioning flexible and rigid pavements. The test for determining the CBR value is typically conducted under soaked conditions and is costly, labour-intensive, and time-
Esteban Díaz, Giovanni Spagnoli
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