A novel hybrid method for crude oil price forecastingJinliang Zhang, Yue‐Jun Zhang, Lu Zhang2015-03-09articleEnergy Economics272 citationsclosedNo readable copy stored yet — this record is metadata only.Stock Market Forecasting MethodsForecasting Techniques and ApplicationsMarket Dynamics and VolatilityAbstractNo abstract was published for this record.Citations in this libraryStored only when both papers are in the corpus — 272 citations exist upstream.References1986Generalized autoregressive conditional heteroskedasticity2008Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm2006Modeling and forecasting petroleum futures volatility2007A new approach for crude oil price analysis based on Empirical Mode Decomposition2012Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology2010Forecasting crude oil market volatility: Further evidence using GARCH-class models2008Forecasting volatility of crude oil markets2010International evidence on crude oil price dynamics: Applications of ARIMA-GARCH modelsCited by2021Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization2024Price forecasting through neural networks for crude oil, heating oil, and natural gas2018A novel multiscale nonlinear ensemble leaning paradigm for carbon price forecasting2018Oil price forecasting using a hybrid model2021Analysis and forecast of China's energy consumption structure2018Forecasting the prices of crude oil: An iterated combination approach2023Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020–20222020A hybrid deep learning approach by integrating LSTM-ANN networks with GARCH model for copper price volatility prediction