A Greylag Goose Optimizer for Enhanced ARIMA-Based Time Series Forecasting of Agricultural Prices

Abstract

Time series forecasting is crucial in agricultural markets, where accurate predictions can enhance stakeholder decision-making. Traditional statistical models such as ARIMA often struggle with capturing complex patterns, necessitating the integration of metaheuristic optimization techniques to improve predictive performance. This study introduces a novel hybrid optimizer, the Grey Wolf-Greylag Goose Optimizer (GGGWO), which combines the strengths of the Grey Wolf Optimizer (GWO) and the Greylag Goose Optimizer (GGO) to enhance the forecasting accuracy of ARIMA models. The proposed GGGWO-ARIMA model is evaluated on a dataset of daily potato prices across multiple Indian cities and is compared against ARIMA, WOA-ARIMA, PSO-ARIMA, GWO-ARIMA, and GGO-ARIMA. Experimental results demonstrate that GGGWO achieves the lowest MSE (0.0027), RMSE (0.0184), and MAE (0.0116) while attaining the highest R-squared (0.9648) and Willmott Index (0.9565), outperforming all baseline models. These findings highlight the efficacy of hybridizing GWO and GGO, offering a robust optimization framework for improving time series forecasting in agricultural price prediction. This can aid policymakers, farmers, and market analysts make data-driven decisions.