Time Series Financial Market Forecasting Based on Machine Learning

Forecasting future values of financial market data, including stock prices, exchange rates, and commodity prices, is a challenging and important task in time series financial market forecasting. Using time series analysis, we presented the Support Vector Machine (SVM) technique for predicting trends in the financial markets. Financial market time series data present a challenge to conventional forecasting techniques due to their complicated patterns and intrinsic dynamic nature. Based on past price and volume data, the SVM algorithm—which is renowned for its resilience and capacity to handle high-dimensional data—is used to forecast future market patterns. The study assesses how well SVM captures non-linear correlations in financial time series while taking fluctuating market and economic situations into account.