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Crude oil prices and kernel-based models

Panella, Massimo; D'Ecclesia, Rita L.; Stack, David G. - In: International journal of financial engineering and risk … 1 (2014) 3, pp. 214-238

Persistent link: https://www.econbiz.de/10010476935

Crude oil price forecasting : experimental evidence from wavelet decomposition and neural network modeling

Jammazi, Rania; Aloui, Chaker - In: Energy economics 34 (2012) 3, pp. 828-841

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Analyzing the robustness of ARIMA and neural networks as a predictive model of crude oil prices

Sharma, Sudhi; Yadav, Miklesh Prasad - In: Theoretical and applied economics : GAER review 27 (2020) 2/623, pp. 289-300

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Interval decomposition ensemble approach for crude oil price forecasting

Sun, Shaolong; Sun, Yuying; Wang, Shouyang; Wei, Yunjie - In: Energy economics 76 (2018), pp. 274-287

Persistent link: https://www.econbiz.de/10011976631

A multiscale time-series decomposition learning for crude oil price forecasting

Tan, Jinghua; Li, Zhixi; Zhang, Chuanhui; Shi, Long; … - In: Energy economics 136 (2024), pp. 1-10

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Is it possible to accurately forecast the evolution of Brent crude oil prices? : an answer based on parametric and nonparametric forecasting methods

Álvarez-Díaz, Marcos - In: Empirical economics : a journal of the Institute for … 59 (2020) 3, pp. 1285-1305

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Artificial Neural Network Models for Forecasting Global Oil Market Volatility

Al-Fattah, Saud - 2013

Energy market volatility affects macroeconomic conditions and can unduly affect the economies of energy-producing countries. Large price swings can be detrimental to both producers and consumers. Market volatility can cause infrastructure and capacity investments to be delayed, employment...

Persistent link: https://www.econbiz.de/10014161014