Short-term forecasting of natural gas prices by using a novel hybrid method based on a combination of the CEEMDAN-SE-and the PSO-ALS-optimized GRU network - Wang, Jun; Cao, Junxing; Yuan, Shan; Cheng, Ming (Energy (Oxford), 2021)
Keywords: Adaptive algorithms; Adaptive learning; Adaptive learning strategy; Adaptive sampling; algorithms; CEEMDAN-SE decomposition; Complex systems; Decomposition; Deep learning; Empirical analysis; energy; energy costs; Entropy; Forecasting; Gasoline prices; GRU neural networks; Machine learning; Mathematical models; Model accuracy; Natural gas; Natural gas price forecasting; Natural gas prices; Natural gas utilities; Network topologies; Particle swarm optimization; prediction; Predictions; Pricing; PSO algorithm; Public utilities; Sequences; Topology; trade; Utilities; Deep learning; Natural gas price forecasting; Adaptive learning strategy; PSO algorithm; CEEMDAN-SE decomposition; GRU neural networks