Energy Exchange on a Melting Glacier

Journal of Japan Society on Water Environment, 1997

Earth-Science Reviews, 2015

Stochastics and Stochastic Reports, 2000

Ekonomia, 1997

Modeling of Adhesively Bonded Joints

Indian Journal of Science and Technology, 2017

Forecasting crude oil prices is essential but usually involves a difficult process. In this paper, we proposed a hybrid Genetic Algorithm and Fast Ensemble Empirical Mode Decomposition (GA-FEEMD) for forecasting crude oil price time series data. The proposed GA-FEEMD basically involves three steps. Firstly, we decomposed the original crude oil price time series data into two Intrinsic Mode Functions (IMFs) using FEEMD algorithm. Then, we forecasted the second IMF which basically is the intrinsic trend of the crude oil prices. Then, we applied Genetic Algorithm (GA) to obtain the stopping criterion in the FEEMD process. The hybrid GA-FEEMD forecasting model was compared with ARIMA and Artificial Neural Network methods. The results showed that the proposed GA-FEEMD model improved the forecasting accuracy of the crude oil price time series data.

1998

Physics Letters A, 1994

OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information), 2016

Boston Hospitality Review, 2020