Predicting Financial Market Movements Through Time Series Analysis and Advanced Machine Learning Algorithms

A novel approach for forecasting the movements of financial markets is presented in this research. It combines Auto Regressive Integrated Moving Average (ARIMA) time series analysis with Random Forest machine learning techniques. Improving predicting accuracy in ever-changing market conditions is the primary goal of the study. Our suggested model achieves better results than current models using a sample dataset. It achieves an MAE of 1.6, an MSE of 2.8, and an RMSE of 1.67. The R-squared value is 0.797, which means that the model explains a significant amount of the variation. This combined methodology takes advantage of ARIMA and Random Forest's complimentary qualities to identify patterns in financial data, whether they are linear or nonlinear. This research adds to the growing body of knowledge that combines machine learning with time series analysis to provide a powerful resource for those making important financial decisions. To further improve the model's flexibility and utility across varied market situations, future research should focus on parameter tweaking, researching other ensembles, and conducting real-world implementation. Finally, this research establishes the groundwork for a novel hybrid model that might greatly enhance the precision of financial market forecasts in the future.