Tata Motors Stock Price Prediction Using Predictive Artificial Intelligence

Predicting stock prices is difficult because it involves navigating a complex environment where nonlinear patterns and constantly shifting market factors shape dynamics. We address the inherent difficulties in stock price forecasting in our research, with a special emphasis on the Tata Motors. Using six models from state-of-the-art deep learning architectures we explore the intricacies of the stock market environment. In this paper, we have applied different machine learning and deep learning models such as Long Short-Term Memory (LSTM), Autoregressive Integrated Moving Average (ARIMA), Prophet and Ensemble Model (combination of Random Forest and Linear Regression) to predict Tata motors stock prices. We have utilized 25 years Tata Motors share details for prediction. Result shows that ARIMA struggles and has an R2 Score of -2.42%, whereas Prophet shows a notable improvement with an R2 Score of 87.82%. It is remarkable that LSTM with no technical indicators reaches an accuracy of 98.44%, while LSTM with technical indications performs even better, reaching 99.25%. The Ensemble Model performs exceptionally well at 99.80% when it combines technical indicators with Random Forest and Linear Regression. With an astounding 99.84 % accuracy rate, the LSTM with Attention Mechanism and Technical Indicators is the best performer. These results highlight the various capacities of each model, highlighting the effectiveness of sophisticated methods in providing accurate and trustworthy forecasts for Tata Motors stock prices, with encouraging paths for further improvements in negotiating the always changing stock market environment.