Evolutionary Neural Network model for West Texas Intermediate crude oil price prediction
By Haruna Chiroma, Sameem Abdulkareem, Tutut Herawan. 2015
173 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Management Science and Operations Research
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Fotios Petropoulos
- Konstantinos Nikolopoulos
- Rob J Hyndman
- Nikolaos Kourentzes
- Vassilios Assimakopoulos
- Evangelos Spiliotis
- Spyros Makridakis
- Christoph Bergmeir
In Management Science and Operations Research
- Fotios Petropoulos
- Rob J Hyndman
- Konstantinos Nikolopoulos
- Nikolaos Kourentzes
- Vassilios Assimakopoulos
- Spyros Makridakis
- Evangelos Spiliotis
- Christoph Bergmeir
Citations in the corpus
- Forecasting Crude Oil Prices: a Deep Learning based Model (2017, methodology): “: + 86-731-58290306 [5] proposed a Genetic Algorithm optimized Neural Network model to forecast the crude oil price fluctuations.”
- Forecasting: theory and practice (2020, background): “studies have increasingly focused on the hybrid forecasting models based on econometrics models and machine learning techniques (Jammazi and Aloui, 2012; He et al., 2012; Baumeister and Kilian, 2015; Chiroma et al., 2015), achieving improved performance. Notably, the vast majority of the literature ”
- A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms (2018, methodology): “Overall, the hybrid methods often imply the combination of interdisciplinary methods to use their strengths and can be roughly classi fi ed into two categories: (1) the combination among AI models, such as the empirical mode decomposition (EMD) based neural network ensemble learning paradigm [25], th”
- A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting (2015, methodology): “[13] presented an evolutionary neural network to predict the WTI monthly crude oil price data from May 1987 to December 2011.”