Predicting Tesla Stock Prices Using AI Techniques: An Analysis of Historical Trends, Trading Volumes, and Market Volatility

Predicting stock market trends, especially for highly volatile stocks such as Tesla, presents unique challenges and opportunities. This study leverages deep learning, machine learning (ML), and time-series analysis as artificial intelligence (AI) tools to forecast Tesla's stock prices. The research aims to create robust predictive models by analyzing historical data, trading volumes, and market volatility [3][16]. The findings provide actionable insights for investors and uncover factors influencing Tesla stock performance. Models such as Long-Short-Term Memory (LSTM), Auto Regressive Integrated Moving Average(ARIMA), Gradient Boosting, and Random Forest are evaluated for prediction precision [1] [17], with SHapley Additive exPlanations(SHAP) employed for feature impact analysis.