Hybrid ARIMA-IIS Approach for Wheat Yield Forecasting: An Integrated Approach

Abstract

This study explores the application of a hybrid approach, combining the Impulse Indicator Saturation (IIS) method with an ARIMA(x) model, to forecast wheat yield. The IIS method is employed to find potential impulse responses, which are then integrated into the ARIMA(x) framework. The IIS method captures the potential joint effects of the weather, climate and other inputs on the data generating process of the wheat yield time series. The performance of the hybrid ARIMA(x) model is compared with that of the standalone ARIMA model using various error metrics, including Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Deviation (MAD). Additionally, model selection criteria such as the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Schwarz Bayesian Information Criterion (SBIC), and Hannan-Quinn Criterion (HQ) are used to identify the optimal model for forecasting. The training data of wheat yield from 1948-2018 was used to fit both the ARIMA and ARIMA(x) models, while the remaining observations until 2023 are used for model validation. The results of the study reveal that the hybrid ARIMA(x) model exhibits superior forecasting ability, demonstrating lower error metrics compared to the standalone ARIMA model. Notably, the ex-ante forecasts for the 2023-24 period predict a wheat production of 29.916 million tons using the ARIMA(x) model and 29.656 million tons using the ARIMA (2,1,2) model. These findings underscore the efficacy of the hybrid approach in enhancing production forecasting accuracy, thereby serving as a valuable basis for early warning systems to address potential demand and supply gaps in wheat production.