Time-Series Foresee Indian Stock Market Trends Based on Statistical Data Using Hybrid Method of Deep Learning

This study presents a powerful LSTM-based model designed to predict trends in the Indian stock market. By using Long Short-Term Memory (LSTM) networks-an advanced type of Recurrent Neural Network (RNN)-the model can effectively analyze time-series data while overcoming issues traditional models face, like ARIMA's difficulty with complex market patterns and the gradient vanishing issue in standard RNNs. By capturing long-term dependencies within a vast historical dataset of over 130,000 records, including stock prices and quotes, this model delivers accurate forecasts tailored to the Indian market. Evaluated with hybrid performance metrics such as Mean Absolute Error(MAE), Root Mean Square Error(RMSE), and confusion matrices, the results highlight the model's reliability and practical value for investors and policymakers alike. This LSTM model provides timely financial insights, enhancing strategic decision-making and risk management. It demonstrates how Hybrid method of Deep learning can uncover unique regional trends in emerging markets, offering a significant step forward from traditional forecasting methods and highlighting the strategic benefits of advanced AI in finance.