Forecasting Nonlinear Crude Oil Futures Prices

By Saeed Moshiri, F. Foroutan. 2006

177 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/36bceb0517517f57a264e1e86f7722297f35e3a0

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

1. Ruijin Du
2. Minggang Wang
3. Lean Yu
4. H. Eugene Stanley
5. Lin Chen
6. Chao Wang
7. Kaijian He
8. Longfeng Zhao

In Management Science and Operations Research

1. Kin Keung Lai
2. Kaijian He
3. H. Eugene Stanley
4. Lean Yu

Citations in the corpus

• A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms (2018, methodology): “Arti fi cial intelligence methods also have disadvantages, For example, ANN and BPNN often su ff er from local minima and over-fi tting, while other AI models, such as SVM and GP including ANN, are sensitive to parameter selection [16 – 24].”
• Crude oil price analysis and forecasting using wavelet decomposed ensemble model (2012, background): “Moshiri and Foroutan [13] proposed a neural network model to model the unknown nonlinear economic structure and demonstrate its effectiveness in performance improvement in empirical studies using crude oil futures prices [13].”