Monthly Henry Hub natural gas spot prices forecasting using variational mode decomposition and deep belief network

[1]  Hossein Hassani,et al.  A statistical approach for a fuel subsidy mechanism , 2018, Energy Policy.

[2]  B. Liseo,et al.  Portfolio Diversification Strategy Via Tail-Dependence Clustering and ARMA-GARCH Vine Copula Approach , 2018, Australian Economic Papers.

[3]  Tiantian Wang,et al.  Financialization, fundamentals, and the time-varying determinants of US natural gas prices , 2019, Energy Economics.

[4]  Yanxue Wang,et al.  Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system , 2015 .

[5]  Jimin Ye,et al.  Crude oil price analysis and forecasting based on variational mode decomposition and independent component analysis , 2017 .

[6]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[7]  Lambros Ekonomou,et al.  Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models , 2008 .

[8]  Nicolas Le Roux,et al.  Representational Power of Restricted Boltzmann Machines and Deep Belief Networks , 2008, Neural Computation.

[9]  The Improvement of Unemployment Rate Predictions Accuracy , 2015 .

[10]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[11]  Yachao Zhang,et al.  Deterministic and probabilistic interval prediction for short-term wind power generation based on variational mode decomposition and machine learning methods , 2016 .

[12]  Narges Salehnia,et al.  Forecasting natural gas spot prices with nonlinear modeling using Gamma test analysis , 2013 .

[13]  Farshad Kowsary,et al.  Multi-objective optimization of the building energy performance: A simulation-based approach by means of particle swarm optimization (PSO) , 2016 .

[14]  Xuyuan Kang,et al.  High-Accuracy Entity State Prediction Method Based on Deep Belief Network Toward IoT Search , 2019, IEEE Wireless Communications Letters.

[15]  Mei Sun,et al.  The spillover effects between natural gas and crude oil markets: The correlation network analysis based on multi-scale approach , 2019, Physica A: Statistical Mechanics and its Applications.

[16]  Jinchao Li,et al.  Monthly crude oil spot price forecasting using variational mode decomposition , 2019, Energy Economics.

[17]  Majid Salari,et al.  An ensemble multi-step-ahead forecasting system for fine particulate matter in urban areas , 2020 .

[18]  Jing Zhang,et al.  Short-term wind speed forecasting based on the Jaya-SVM model , 2020 .

[19]  Zhang Yang,et al.  Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods , 2017 .

[20]  Anangela Garcia,et al.  Forecasting US Natural Gas Production into year 2020: a comparative study. , 2004 .

[21]  Xiaoyi Mu Weather, storage, and natural gas price dynamics: Fundamentals and volatility , 2007 .

[22]  F. H. Jufri,et al.  Day-Ahead System Marginal Price Forecasting Using Artificial Neural Network and Similar-Days Information , 2019, Journal of Electrical Engineering & Technology.

[23]  Zbigniew Michalewicz,et al.  Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review , 2017, Evolutionary Computation.

[24]  Jimin Ye,et al.  Energy price prediction based on independent component analysis and gated recurrent unit neural network , 2019 .

[25]  Chi-Keung Woo,et al.  Market efficiency, cross hedging and price forecasts: California's natural-gas markets , 2006 .

[26]  Dominique Zosso,et al.  Variational Mode Decomposition , 2014, IEEE Transactions on Signal Processing.

[27]  Salman Mohagheghi,et al.  Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems , 2008, IEEE Transactions on Evolutionary Computation.

[28]  Richard A. Startzman,et al.  Predicting Natural Gas Production Using Artificial Neural Network , 2001 .

[29]  Vladimir Ceperic,et al.  Short-term forecasting of natural gas prices using machine learning and feature selection algorithms , 2017 .

[30]  I. Jamali,et al.  Predicting daily oil prices: Linear and non-linear models , 2018, Research in International Business and Finance.

[31]  Sida Feng,et al.  Three-level network analysis of the North American natural gas price: A multiscale perspective , 2020 .

[32]  Apostolos Serletis,et al.  Testing for Common Features in North American Energy Markets , 2004 .

[33]  Ali Ahmadian,et al.  A Novel Electricity Price Forecasting Approach Based on Dimension Reduction Strategy and Rough Artificial Neural Networks , 2020, IEEE Transactions on Industrial Informatics.

[34]  Ian T. Nabney,et al.  Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models , 2010 .

[35]  Ali Azadeh,et al.  A hybrid neuro-fuzzy simulation approach for improvement of natural gas price forecasting in industrial sectors with vague indicators , 2012 .

[36]  Amitava Chatterjee,et al.  Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization , 2006, Comput. Oper. Res..

[37]  M. Malliaris,et al.  Forecasting inter-related energy product prices , 2008 .

[38]  F. Diebold,et al.  Comparing Predictive Accuracy , 1994, Business Cycles.

[39]  Yi-Ming Wei,et al.  An adaptive hybrid model for short term electricity price forecasting , 2020 .

[40]  Ying Fan,et al.  The Behaviour Mechanism Analysis of Regional Natural Gas Prices: A Multi-Scale Perspective , 2016 .

[41]  Ming Li,et al.  Variational mode decomposition denoising combined the detrended fluctuation analysis , 2016, Signal Process..