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.
Highlights
Several companies since 1907 have attempted to discover oil that has commercial value, but failed [1]
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 ARIMA (1, 1, 0), ANN (4, 1, 1), RF and FTS models were compared in both the training and the test sets using the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Nash-Sutcliffe Efficiency (NSE) performance measures to get a high-performance model
Summary
Several companies since 1907 have attempted to discover oil that has commercial value, but failed [1]. There is a significant decrease in the price of crude oil and because Nigeria is a mono-product country that relies heavily on the oil sector, it has a negative impact on the Nigerian economy. For her economy, this assertion is unchallengeable and many researchers such as [15] [16] have used a variety of traditional statistical models to fit the Nigerian Bonny Light crude oil price but the application of machine learning models and FTS on the crude oil price has been grossly understudied. A percentage increase in oil prices raises the price of used energy
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