Towards Predictive Crude Oil Purchase: A Case Study in the USA and Europe
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 mo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/11/4003 |
| _version_ | 1797493573812224000 |
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| author | Jen-Yu Lee Tien-Thinh Nguyen Hong-Giang Nguyen Jen-Yao Lee |
| author_facet | Jen-Yu Lee Tien-Thinh Nguyen Hong-Giang Nguyen Jen-Yao Lee |
| author_sort | Jen-Yu Lee |
| collection | DOAJ |
| 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo> </mo><msub><mrow><mi>MAPE</mi></mrow><mrow><mi>Europe</mi><mo>−</mo><mi>ARIMA</mi></mrow></msub></mrow></semantics></math></inline-formula> = 0.05, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>MAPE</mi></mrow><mrow><mi>USA</mi><mo>−</mo><mi>ARIMA</mi></mrow></msub><mo>=</mo><mn>0.05</mn></mrow></semantics></math></inline-formula>. Based on these accuracy parameters, the forecasting models appear incredibly reliable; similarly, the study results might assist governing bodies in making significant decisions, thereby accelerating socio-economic development in the world’s two largest economies. |
| first_indexed | 2024-03-10T01:21:57Z |
| format | Article |
| id | doaj.art-6e21de7acf45416c9ed1b72c7fc71204 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T01:21:57Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-6e21de7acf45416c9ed1b72c7fc712042023-11-23T13:58:44ZengMDPI AGEnergies1996-10732022-05-011511400310.3390/en15114003Towards Predictive Crude Oil Purchase: A Case Study in the USA and EuropeJen-Yu Lee0Tien-Thinh Nguyen1Hong-Giang Nguyen2Jen-Yao Lee3Department of Statistics, Feng Chia University, Taichung City 407102, TaiwanDepartment of International Business, National Kaohsiung University of Science and Technology, Kaohsiung 807618, TaiwanFaculty of Architecture, Thu Dau Mot University, Thu Dau Mot 820000, VietnamDepartment of International Business, National Kaohsiung University of Science and Technology, Kaohsiung 807618, TaiwanCrude 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo> </mo><msub><mrow><mi>MAPE</mi></mrow><mrow><mi>Europe</mi><mo>−</mo><mi>ARIMA</mi></mrow></msub></mrow></semantics></math></inline-formula> = 0.05, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>MAPE</mi></mrow><mrow><mi>USA</mi><mo>−</mo><mi>ARIMA</mi></mrow></msub><mo>=</mo><mn>0.05</mn></mrow></semantics></math></inline-formula>. Based on these accuracy parameters, the forecasting models appear incredibly reliable; similarly, the study results might assist governing bodies in making significant decisions, thereby accelerating socio-economic development in the world’s two largest economies.https://www.mdpi.com/1996-1073/15/11/4003crude oil purchase priceforecastingARIMA modelSARIMA model |
| spellingShingle | Jen-Yu Lee Tien-Thinh Nguyen Hong-Giang Nguyen Jen-Yao Lee Towards Predictive Crude Oil Purchase: A Case Study in the USA and Europe Energies crude oil purchase price forecasting ARIMA model SARIMA model |
| title | Towards Predictive Crude Oil Purchase: A Case Study in the USA and Europe |
| title_full | Towards Predictive Crude Oil Purchase: A Case Study in the USA and Europe |
| title_fullStr | Towards Predictive Crude Oil Purchase: A Case Study in the USA and Europe |
| title_full_unstemmed | Towards Predictive Crude Oil Purchase: A Case Study in the USA and Europe |
| title_short | Towards Predictive Crude Oil Purchase: A Case Study in the USA and Europe |
| title_sort | towards predictive crude oil purchase a case study in the usa and europe |
| topic | crude oil purchase price forecasting ARIMA model SARIMA model |
| url | https://www.mdpi.com/1996-1073/15/11/4003 |
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