Volume 6 • Issue 1 • PP: 2 9– 39 • 2026
Enhanced Stock Price Forecasting: Time Series Analysis with ARIMA and FGGO Optimization
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Forecasting financial markets remains a persistent challenge due to the nonlinear, stochastic, and nonstationary nature of stock price dynamics. This study is motivated by the need to enhance the robustness and adaptability of traditional statistical forecasting models through intelligent optimization. We propose an advanced hybrid framework that integrates the AutoRegressive Integrated Moving Average (ARIMA) model with the Fitness Greylag Goose Optimization (FGGO) algorithm—a refined metaheuristic inspired by collective behavioral intelligence and adaptive search strategies. The primary contribution of this research lies in the methodological fusion of classical time series modeling with dynamic metaheuristic optimization to improve predictive accuracy, convergence stability, and resistance to local optima. Comparative experiments on the historical stock prices of PT Bank Central Asia Tbk (BBCA.JK) demonstrate a substantial performance uplift: the baseline ARIMA model achieved a Mean Squared Error (MSE) of 0.0333, whereas the FGGO-optimized ARIMA reduced the MSE dramatically to 0.0038, outperforming other optimization techniques such as the Genetic Algorithm (GA), Whale Optimization Algorithm (WOA), and Particle Swarm Optimization (PSO). These results confirm that FGGO significantly enhances ARIMA’s capacity to capture intricate temporal dependencies and volatile market structures. The implications of this study extend beyond finance, offering a scalable, explainable, and high performance optimization paradigm for diverse time series forecasting applications in economics, engineering, and intelligent decision-support systems.
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