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Towards Predictive Crude Oil Purchase: A Case Study in the USA and Europe
| Content Provider | MDPI |
|---|---|
| Author | Lee, Jen-Yu Nguyen, Tien-Thinh Nguyen, Hong-Giang Lee, Jen-Yao |
| Copyright Year | 2022 |
| Description | Crude oil price volatility impacts the global economy in general, as well as the economies of Europe and the United States in particular; it is supremely difficult to describe its tendency precisely, hence it leads to a forecasting methodology. This study aims to use the autoregressive integrated moving average (ARIMA), and seasonal autoregressive integrated moving average (SARIMA) approaches to cope with this problem in the United States and Europe. The data was gathered from the U.S. Energy Information Administration and federal research economic data (FRED) from January 2017 to September 2021. Simultaneously, values from January 2017 to March 2021, with 51 observations accounting for 90% of the total samples, were employed for the training phase, and the rest were used for the testing phase. The forecast result also indicated that the root mean square error (RMSE) and mean absolute percentage error (MAPE) values, applied by ARIMA models in Europe and the United States, have higher accurate indicators than SARIMA models. As a result, the ARIMA model achieved the best accuracy in both Europe and the USA, with |
| Starting Page | 4003 |
| e-ISSN | 19961073 |
| DOI | 10.3390/en15114003 |
| Journal | Energies |
| Issue Number | 11 |
| Volume Number | 15 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-05-29 |
| Access Restriction | Open |
| Subject Keyword | Energies Petroleum Engineering Crude Oil Purchase Price Forecasting Arima Model Sarima Model |
| Content Type | Text |
| Resource Type | Article |
