A non-iterative decomposition-ensemble learning paradigm using RVFL network for crude oil price forecasting

2018 ◽  
Vol 70 ◽  
pp. 1097-1108 ◽  
Author(s):  
Ling Tang ◽  
Yao Wu ◽  
Lean Yu
Keyword(s):  
Crude Oil ◽  
Ensemble Learning ◽  
Oil Price ◽  
Price Forecasting ◽  
Crude Oil Price ◽  
Learning Paradigm ◽  
Iterative Decomposition

A hybrid grid-GA-based LSSVR learning paradigm for crude oil price forecasting

2015 ◽  
Vol 27 (8) ◽  
pp. 2193-2215 ◽  
Author(s):  
Lean Yu ◽  
Wei Dai ◽  
Ling Tang ◽  
Jiaqian Wu
Keyword(s):  
Crude Oil ◽  
Oil Price ◽  
Price Forecasting ◽  
Crude Oil Price ◽  
Learning Paradigm ◽  
Hybrid Grid

A Novel CEEMD-Based EELM Ensemble Learning Paradigm for Crude Oil Price Forecasting

2015 ◽  
Vol 14 (01) ◽  
pp. 141-169 ◽  
Author(s):  
Ling Tang ◽  
Wei Dai ◽  
Lean Yu ◽  
Shouyang Wang
Keyword(s):  
Crude Oil ◽  
Empirical Mode Decomposition ◽  
Ensemble Learning ◽  
Prediction Accuracy ◽  
Oil Price ◽  
Series Data ◽  
The Novel ◽  
Crude Oil Price ◽  
Learning Paradigm ◽  
Mode Decomposition

To enhance the prediction accuracy for crude oil price, a novel ensemble learning paradigm coupling complementary ensemble empirical mode decomposition (CEEMD) and extended extreme learning machine (EELM) is proposed. This novel method is actually an improved model under the effective "decomposition and ensemble" framework, especially for nonlinear, complex, and irregular data. In this proposed method, CEEMD, a current extension from the competitive decomposition family of empirical mode decomposition (EMD), is first applied to divide the original data (i.e., difficult task) into a number of components (i.e., relatively easy subtasks). Then, EELM, a recently developed, powerful, fast and stable intelligent learning technique, is implemented to predict all extracted components individually. Finally, these predicted results are aggregated into an ensemble result as the final prediction using simple addition ensemble method. With the crude oil spot prices of WTI and Brent as sample data, the empirical results demonstrate that the novel CEEMD-based EELM ensemble model statistically outperforms all listed benchmarks (including typical forecasting techniques and similar ensemble models with other decomposition and ensemble tools) in prediction accuracy. The results also indicate that the novel model can be used as a promising forecasting tool for complicated time series data with high volatility and irregularity.

scholarly journals Optimizing the monthly crude oil price forecasting accuracy via bagging ensemble models

2015 ◽  
Vol 7 (5) ◽  
pp. 127-136 ◽  
Author(s):  
Yumurtaci Aydo mu Hacer ◽  
Ekinci Aykut ◽  
Erdal Halil ◽  
Erdal Hamit
Keyword(s):  
Crude Oil ◽  
Oil Price ◽  
Price Forecasting ◽  
Crude Oil Price ◽  
Forecasting Accuracy ◽  
Ensemble Models ◽  
Bagging Ensemble

scholarly journals A Research on Markov Model and Hidden Markov Model in Crude Oil Price Forecasting for PETRONAS Malaysia

2019 ◽  
Vol 8 (12S2) ◽  
pp. 121-125 ◽  
Keyword(s):  
Markov Model ◽  
Crude Oil ◽  
Hidden Markov Model ◽  
Structural Equation ◽  
Hidden Markov ◽  
Oil Price ◽  
Price Forecasting ◽  
Crude Oil Price ◽  
Crude Oil Prices ◽  
Accurate Forecast

Crude oil price forecasting is an essential component of sustainable development of many countries as crude oil is an unavoidable product that exists on earth. In this paper, a model based on a hidden Markov model and Markov model for crude oil price forecasting was developed, and their relative performance was compared. Path analysis of Structural Equation Modelling was employed to model the effects of forecasted prices and the actual crude oil price to get the most accurate forecast. The key variables used to develop the models were monthly crude oil prices s from PETRONAS Malaysia. It was found that the hidden Markov model was more accurate than the Markov model in forecasting the crude oil price. The findings of this study show that the hidden Markov model is a potentially promising method of crude oil price forecasting that merit further study.

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