Modeling the Nigerian Bonny Light Crude Oil Price: The Power of Fuzzy Time Series ()
ABSTRACT
Several authors have used different classical statistical models to fit
the Nigerian Bonny Light crude oil price but the application of machine
learning models and Fuzzy Time Series model on the crude oil price has been
grossly understudied. Therefore, in this study, a classical statistical model—Autoregressive Integrated Moving Average (ARIMA),
two machine learning models—Artificial
Neural Network (ANN) and Random Forest (RF) and Fuzzy Time Series (FTS) Model
were compared in modeling the Nigerian Bonny Light crude oil price data for the
periods from January, 2006 to December, 2020. The monthly secondary data were
collected from the Nigerian National Petroleum Corporation (NNPC) and Reuters
website and divided into train (70%) and test (30%) sets. The train set was
used in building the models and the models were validated using the test set. The
performance measures used for the comparison include: The modified
Diebold-Mariano test, the Root Mean Square Error (RMSE), the Mean Absolute
Percentage Error (MAPE) and Nash-Sutcliffe Efficiency (NSE) values. Based on
the performance measures, ANN (4, 1, 1) and RF performed better than ARIMA (1, 1,
0) model but FTS model using Chen’s algorithm outperformed every other model.
The results recommend the use of FTS model for forecasting future values of the
Nigerian Bonny Light Crude oil. However, a hybrid model of ARIMA-ANN or
ARIMA-RF should be built and compared with Chen’s algorithm FTS model for the
same data set to further verify the power of FTS model using Chen’s algorithm.
Share and Cite:
Bartholomew, D. , Orumie, U. , Obite, C. , Duru, B. and Akanno, F. (2021) Modeling the Nigerian Bonny Light Crude Oil Price: The Power of Fuzzy Time Series.
Open Journal of Modelling and Simulation,
9, 370-3900. doi:
10.4236/ojmsi.2021.94024.