Non-Linear Stock Market Prediction with Support Vector Machines

Abstract

In forecasting financial markets using time series data, predicting Future in the financial market data such as Stock prices, currency exchange commodity price, and rates is a difficult but crucial endeavor. We introduced the Support Vector Machine (SVM) technique for financial market trend prediction using TSA. Because of their intricate patterns and inherent dynamic character, financial market time series data pose a challenge to traditional forecasting methods. The SVM algorithm, which is well-known for its robustness and ability to handle high-dimensional data, is used to estimate future market patterns based on historical price and volume data. The study evaluates the effectiveness of SVM in capturing non-linear correlations in financial time while accounting for shifting market and economic conditions. Through in-depth empirical investigation and performance comparison with other forecasting models, this study sheds light on the suitability and precision of SVM in anticipating movements in financial markets. For traders, investors, and scholars studying algorithmic trading and quantitative finance, it has significant ramifications.