Enhanced Stock Price Forecasting: Time Series Analysis with

ARIMA and FGGO Optimization

Laith Farhan1,* Raad S. Alhumaima2

1 School of Engineering, Manchester Metropolitan University, Manchester, M1, UK

2 Brunel University, Uxbridge UB8 3PH, U.K.

Received: January 28, 2026 Revised: March 15, 2026 Accepted: May 12, 2026 ⋆ Corresponding author

ABSTRACT

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.

Keywords: Financial Time Series Forecasting AutoRegressive Integrated Moving Average (ARIMA) Fitness Greylag

Goose Optimization (FGGO) Metaheuristic Optimization Hybrid Predictive Modeling

1. INTRODUCTION

The issue of financial market prediction has been identified as

one of the most complex and challenging aspects in modern

financial analysis. It is complicated by the fact that volatile

markets are characterized by the complex interaction of unpredictable

and changing factors, including macroeconomic

conditions, geopolitical events, corporate performance metrics,

and the moods, prejudices, and behavioral patterns of

investors themselves [1]. The externalities can lead to disproportionately

large impacts on asset prices, which can include

unexpected policy changes, global crises, or even relatively

environmental changes. This can be attributed to the nonlinear

and highly dynamic nature of financial markets, such that

perturbations can cause various prices to change in a cascading

manner relatively quickly. This suggests that statistical

and computational issues are also associated with forecasting

financial time series, which is likely to be the case in other

areas of predictive analytics [2].

Traditional methods of financial forecasting include econo-