Time Series forecasting global price of bananas using Hybrid ARIMA-NARNN model

Abstract

<abstract> <p>This study tried to demonstrate the role of Time Series models in a modeling and forecasting process using publicly available long-term records of monthly global price of bananas during the period of January 1990 to November 2021 reported in the International Monetary Fund. Following the Box–Jenkins methodology, an ARIMA (2,1,4) with a drift model was selected as the best-fit model for the Time Series, according to its lowest AIC value. Using the Levenberg-Marquardt algorithm, the results revealed that the NARNN model with 12 neurons in the hidden layer and 6 times delays provided the best performance in the nonlinear autoregressive neural network models at its smaller MSE value. The ARIMA and NARNN models are good at modelling linear and nonlinear problems for the Time Series, respectively. However, using the HYBRID model, a combination of the ARIMA and NARNN models that has both linear and nonlinear modeling capabilities can be a better choice for modeling the Time Series. The comparative results revealed that the HYBRID model with 11 neurons in the hidden layer and 3 times delays yielded higher accuracy than the NARNN model with 12 neurons in the hidden layer and 6 times delays, and the ARIMA (2,1,4) with a drift model, according to its lowest MSE in this study.</p> </abstract>