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

Chi Yeong Nain () and Chi Orson ()
Additional contact information
Chi Yeong Nain: University of Maryland Eastern Shore, Princess Anne, MD, U.S.A. Department of Agriculture, Food and Resource Sciences
Chi Orson: University of Maryland Eastern Shore, Princess Anne, MD, U.S.A. Cybersecurity Engineering Technology Program

Econometrics. Advances in Applied Data Analysis, 2021, vol. 25, issue 3, 21-41

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.

Keywords: bananas; global price; time series; modeling; forecasting; seasonal ARIMA; multilayer perceptron neural network (search for similar items in EconPapers)
JEL-codes: C22 C45 C53 Q11 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.15611/eada.2021.3.02 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:vrs:eaiada:v:25:y:2021:i:3:p:21-41:n:2

DOI: 10.15611/eada.2021.3.02

Access Statistics for this article

Econometrics. Advances in Applied Data Analysis is currently edited by Józef Dziechciarz

More articles in Econometrics. Advances in Applied Data Analysis from Sciendo
Bibliographic data for series maintained by Peter Golla ().