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

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  • 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)

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.

Suggested Citation

  • Chi Yeong Nain & Chi Orson, 2021. "Modeling and Forecasting of Monthly Global Price of Bananas Using Seasonal Arima and Multilayer Perceptron Neural Network," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 25(3), pages 21-41, September.
    • Handle: RePEc:vrs:eaiada:v:25:y:2021:i:3:p:21-41:n:2
    DOI: 10.15611/eada.2021.3.02
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    More about this item

    Keywords

    bananas; global price; time series; modeling; forecasting; seasonal ARIMA; multilayer perceptron neural network;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q11 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Aggregate Supply and Demand Analysis; Prices

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