@misc{Chi_Yeong_Nain_Modeling_2021,
 author={Chi, Yeong Nain and Chi, Orson},
 identifier={DOI: 10.15611/eada.2021.3.02},
 year={2021},
 rights={Pewne prawa zastrzeżone na rzecz Autorów i Wydawcy},
 publisher={Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu},
 description={Econometrics = Ekonometria, 2021, Vol. 25, No. 3, s. 21-41},
 language={eng},
 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},
 title={Modeling and forecasting of monthly global price of bananas using seasonal ARIMA and multilayer perceptron neural network},
 type={artykuł},
 keywords={bananas, global price, time series, modeling, forecasting, seasonal ARIMA, multilayer perceptron neural network, banany, cena globalna, szeregi czasowe, modelowanie, prognozowanie, sezonowy model ARIMA, wielowarstwowa sieć nauronowa perceptronowa},
}