Financial Time Series Forecasting: A Comparison Between Traditional Methods and AI-Driven Techniques

Financial time series forecasting plays a crucial role in predicting future market trends, pricing assets, and managing risks in financial markets. This paper compares traditional methods, such as ARIMA, Exponential Smoothing, and GARCH, with AI-driven techniques, including machine learning and deep learning models, for financial time series forecasting. Traditional models are well-established and effective for stationary data, but they struggle with non-linear relationships and large datasets. In contrast, AI-driven techniques, such as Random Forests, Long Short-Term Memory Networks (LSTMs), and reinforcement learning, offer improved accuracy and adaptability by capturing complex patterns in the data. However, these models come with higher computational complexity and challenges related to interpretability. The paper provides a comprehensive comparison of these methods, highlighting their strengths, limitations, and practical applications. It concludes by offering recommendations for when to use traditional methods versus AI-driven approaches, based on the nature of the data and forecasting needs. The integration of AI with traditional models is also discussed as a promising future direction in financial forecasting.