Modeling and Forecasting of Monthly Global Price of Bananas Using Seasonal ARIMA and Multilayer Perceptron Neural Network
The primary purpose of this study was to pursue the analysis of the time series data and to demonstrate the role of time series model in the predicting process using long-term records of the monthly global price of bananas from January 1990 to November 2020. Following the Box-Jenkins methodology, AR...
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| Format: | Article |
| Language: | English |
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Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu
2021-01-01
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| Series: | Ekonometria |
| Online Access: | https://journals.ue.wroc.pl/eada/article/view/947 |
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| author | Yeong Nain Chi Orson Chi |
| author_facet | Yeong Nain Chi Orson Chi |
| author_sort | Yeong Nain Chi |
| collection | DOAJ |
| description | The primary purpose of this study was to pursue the analysis of the time series data and to demonstrate the role of time series model in the predicting process using long-term records of the monthly global price of bananas from January 1990 to November 2020. Following the Box-Jenkins methodology, ARIMA(4,1,2)(1,0,1)[12] with the drift model was selected to be the best fit model for the time series, according to the lowest AIC value in this study. Empirically, the results revealed that the MLP neural network model performed better compared to ARIMA(4,1,2)(1,0,1)[12] with the drift model at its smaller MSE value. Hence, the MLP neural network model can provide useful information important in the decision-making process related to the impact of the change of the future global price of bananas. Understanding the past global price of bananas is important for the analyses of current and future changes of global price of bananas. In order to sustain these observations, research programs utilizing the resulting data should be able to improve significantly our understanding and narrow projections of the future global price of bananas.(original abstract) |
| format | Article |
| id | doaj-art-2a8e3408944841e98d48e164be565997 |
| institution | OA Journals |
| issn | 2449-9994 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu |
| record_format | Article |
| series | Ekonometria |
| spelling | doaj-art-2a8e3408944841e98d48e164be5659972025-08-20T02:09:12ZengWydawnictwo Uniwersytetu Ekonomicznego we WrocławiuEkonometria2449-99942021-01-01nr 3948Modeling and Forecasting of Monthly Global Price of Bananas Using Seasonal ARIMA and Multilayer Perceptron Neural NetworkYeong Nain Chi0Orson Chi1University of Maryland Eastern Shore, Princess Anne, MD, U.S.A.University of Maryland Eastern Shore, Princess Anne, MD, U.S.A.The primary purpose of this study was to pursue the analysis of the time series data and to demonstrate the role of time series model in the predicting process using long-term records of the monthly global price of bananas from January 1990 to November 2020. Following the Box-Jenkins methodology, ARIMA(4,1,2)(1,0,1)[12] with the drift model was selected to be the best fit model for the time series, according to the lowest AIC value in this study. Empirically, the results revealed that the MLP neural network model performed better compared to ARIMA(4,1,2)(1,0,1)[12] with the drift model at its smaller MSE value. Hence, the MLP neural network model can provide useful information important in the decision-making process related to the impact of the change of the future global price of bananas. Understanding the past global price of bananas is important for the analyses of current and future changes of global price of bananas. In order to sustain these observations, research programs utilizing the resulting data should be able to improve significantly our understanding and narrow projections of the future global price of bananas.(original abstract)https://journals.ue.wroc.pl/eada/article/view/947 |
| spellingShingle | Yeong Nain Chi Orson Chi Modeling and Forecasting of Monthly Global Price of Bananas Using Seasonal ARIMA and Multilayer Perceptron Neural Network Ekonometria |
| title | Modeling and Forecasting of Monthly Global Price of Bananas Using Seasonal ARIMA and Multilayer Perceptron Neural Network |
| title_full | Modeling and Forecasting of Monthly Global Price of Bananas Using Seasonal ARIMA and Multilayer Perceptron Neural Network |
| title_fullStr | Modeling and Forecasting of Monthly Global Price of Bananas Using Seasonal ARIMA and Multilayer Perceptron Neural Network |
| title_full_unstemmed | Modeling and Forecasting of Monthly Global Price of Bananas Using Seasonal ARIMA and Multilayer Perceptron Neural Network |
| title_short | Modeling and Forecasting of Monthly Global Price of Bananas Using Seasonal ARIMA and Multilayer Perceptron Neural Network |
| title_sort | modeling and forecasting of monthly global price of bananas using seasonal arima and multilayer perceptron neural network |
| url | https://journals.ue.wroc.pl/eada/article/view/947 |
| work_keys_str_mv | AT yeongnainchi modelingandforecastingofmonthlyglobalpriceofbananasusingseasonalarimaandmultilayerperceptronneuralnetwork AT orsonchi modelingandforecastingofmonthlyglobalpriceofbananasusingseasonalarimaandmultilayerperceptronneuralnetwork |