Forecasting energy commodity prices: A large global dataset sparse approach

Business & Economics

• Energy Forecasting 100%
• Commodity Prices 61%
• Commodities 56%
• Data Base 55%
• Global Vector Autoregressive Model 47%
• Energy 47%
• Machine Learning 40%
• Latent Factors 40%
• Dynamic Factor Model 39%
• Random Walk Model 39%
• Factor Loadings 38%
• Predictability 38%
• Gas Price 37%
• Large Economy 35%
• Energy Prices 32%
• Maximum Likelihood 30%
• Macroeconomic Variables 29%
• Gas 24%
• Benchmark 23%
• Oil 22%

Engineering & Materials Science

• Gas oils 16%
• Random forests 16%
• Maximum likelihood 14%
• Coal 12%
• Machine learning 11%
• Gases 8%