<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</abstract>

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.

Modeling and Forecasting of Monthly Global Price of Bananas Using Seasonal Arima and Multilayer Perceptron Neural Network

Department of Agriculture, Food and Resource Sciences

Cybersecurity Engineering Technology Program

Econometrics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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...

Modeling and Forecasting of Monthly Global Price of Bananas Using Seasonal Arima and Multilayer Perceptron Neural Network

Publié en ligne: 08 oct. 2021

Pages: 21 - 41

DOI: https://doi.org/10.15611/eada.2021.3.02

Mots clés
bananas, global price, time series, modeling, forecasting, seasonal ARIMA, multilayer perceptron neural network

© 2021 Yeong Nain Chi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Modeling and Forecasting of Monthly Global Price of Bananas Using Seasonal Arima and Multilayer Perceptron Neural Network

Publié en ligne: 08 oct. 2021

Pages: 21 - 41

DOI: https://doi.org/10.15611/eada.2021.3.02

Mots clésbananas, global price, time series, modeling, forecasting, seasonal ARIMA, multilayer perceptron neural network

© 2021 Yeong Nain Chi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
bananas, global price, time series, modeling, forecasting, seasonal ARIMA, multilayer perceptron neural network