Prediction of the SYM-H Index Using a Bayesian Deep Learning Method With Uncertainty Quantification

Keyphrases

• Deep Learning Methods 100%
• Uncertainty Quantification 100%
• SYM-H 100%
• Hirsch Index 100%
• Bayesian Deep Learning 100%
• Forecast Skill Score 57%
• Large Storm 28%
• Uncertain Data 28%
• Model Uncertainty 28%
• Gradient Boosting Machine 28%
• Parameter Values 14%
• Space Science 14%
• Interplanetary Magnetic Field 14%
• Storm Time 14%
• Machine Learning Techniques 14%
• Learning Framework 14%
• Graph Neural Network 14%
• Science Data 14%
• Bayesian Inference 14%
• Bidirectional Long Short-term Memory (BiLSTM) 14%
• Index-based 14%
• Magnetic Field Parameter 14%
• Quiet Time 14%
• Short-term Forecasting 14%
• Deep Learning Framework 14%
• Predicting the Future 14%
• Related Machines 14%
• Model Uncertainty Quantification 14%
• Solar Wind Magnetic Field 14%
• Epistemic Models 14%
• Aleatoric 14%

Engineering

• Deep Learning 100%
• Uncertainty Quantification 100%
• Model Uncertainty 100%
• Magnetic Field 50%
• Experimental Result 50%
• NASA 50%
• Machine Learning Method 50%
• Long Short-Term Memory 50%
• Term Forecast 50%

Earth and Planetary Sciences

• Uncertainty Modeling 100%
• Solar Wind 50%
• Interplanetary Magnetic Field 50%
• Machine Learning 50%
• Time Series 50%