AI-Driven Time Series Forecasting for Financial Markets: Leveraging Machine Learning for Smarter Predictions

Authors- Mrs.V.Anantha Lakshmi, S.Venkata Basavayya, Y.S.Santosh Kumar, P.Bhanu Divyasri, V.Sandeep, S.Likhita

Abstract-Financial markets, including stock prices, exchange rates, and commodity prices, are inherently volatile and influenced by numerous factors, making their prediction a challenging yet essential task. Accurate forecasting of market trends is crucial for investors, financial analysts, and policymakers, as it helps in making informed decisions and mitigating risks. In this study, we explore the use of Support Vector Machine (SVM), a powerful machine learning algorithm, for time series forecasting of financial market trends. Traditional forecasting methods often struggle with financial data due to its non-linear and dynamic nature. However, SVM is well-known for its ability to handle high-dimensional data and capture complex patterns, making it a suitable choice for financial market prediction. Our approach leverages historical price and volume data to train the SVM model, enabling it to recognize patterns and predict future market movements. The study evaluates how effectively SVM adapts to changing market conditions, demonstrating its ability to model non-linear relationships within financial time series. Additionally, we consider external economic factors that may influence market behavior, further validating the robustness of the model. The findings highlight the potential of SVM in financial forecasting, offering a reliable alternative to traditional methods. Future work may involve integrating hybrid models combining SVM with deep learning techniques or incorporating macro-economic indicators to further enhance prediction accuracy. This research contributes to the growing field of AI-driven financial analysis, paving the way for more sophisticated and data-driven investment strategies.

DOI: /10.61463/ijset.vol.13.issue2.259