| A Greylag Goose Optimizer for Enhanced ARIMA-Based Time Series Forecasting of Agricultural Prices |
| Paper ID : 1004-ICEEM2025 (R2) |
| Authors |
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Nima Khodadadi1, Marwa M. Eid2, Khaled Sh. Gaber3, Mahmoud Elshabrawy Mohamed4, Mai Elazab5, El-Sayed M. El-Kenawy *6 1Department of Civil and Architectural Engineering University of Miami, Coral Gables, FL, USA 2Faculty of Artificial Intelligence Delta University for Science and Technology, Mansoura 11152, Egypt 3Computer Science and Intelligent Systems Research Center Blacksburg 24060, Virginia, USA 4Computer Engineering and Control Systems Dept. Faculty of Engineering, Mansoura University, Egypt 5Dept. of Communications and Electronics Engineering Delta Higher Institute of Engineering and Technology, Mansoura, Egypt 6Delta Higher Institute for Engineering and Technology |
| 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 forecast-ing accuracy of ARIMA models. The proposed GGGWO-ARIMA model is evaluated on a dataset of daily potato prices across multi-ple 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. |
| Keywords |
| Metaheuristic Optimization, Time Series Forecasting, Grey Wolf Optimizer (GWO), Greylag Goose Optimizer (GGO), Hybrid ARIMA Model |
| Status: Accepted |