Volume 12 • Issue 1 • PP: 42–54 • 2027
Hybrid Metaheuristic Optimization for Time Series Forecasting: Integrating Grey Wolf and Greylag Goose Strategies
Open Access & Copyright
© 2027 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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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.
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