A Novel Approach for Accurate Stock Market Forecasting by Integrating ARIMA and XGBoost

In today's economy, the prediction and analysis of stock market data play a crucial role. Over the last decade, researchers have employed the neural network, an intelligent data mining technique, across various domains. Forecasting algorithms can be broadly classified into linear models and non-linear models. This paper employs the Autoregressive Integrated Moving Average (ARIMA) linear model to develop a robust predictive framework for forecasting stock market trends. This study introduces an innovative method by combining the ARIMA algorithm with XGBoost machine learning to create an advanced predictive model for stock market trends. By integrating temporal dependency capture from ARIMA with the improved predictive capabilities of the machine-learning model, this approach aims to enhance the accuracy and effectiveness of stock market trend predictions. Leveraging the strengths of both methodologies, the hybrid model exhibits superior accuracy, surpassing standalone ARIMA and XGBoost models. Training the model involves utilizing historical stock prices, trading volumes, and pertinent financial indicators. Evaluation will encompass the Google stock dataset to estimate the model's performance across various market conditions. By analyzing diverse historical stock market data, including prices, trading volumes, and financial indicators, the integrated model achieves a remarkable prediction accuracy of over 96.7%. The results underscore the efficacy of combining traditional time series analysis with advanced machine learning, positioning the ARIMA-XGBoost hybrid model as a potent tool for precise and robust stock market trend forecasting.