Time Series Data Analysis and Prediction of Stock Prices Using Deep Learning Models

The objective of this paper is to develop a reliable method for predicting NSE (National Stock Exchange) stock prices in India using deep learning models. The purpose of this model is to enhance the precision of time series data management by developing an advanced hybrid multistep-ahead CNN-LSTM model, which is capable of real-time time series forecasting with high accuracy. Learnable sets are trained and tested using commonly recognized error measures, such as accuracy, and the model is fine-tuned using 5-minute interval NSE stock data. A thorough examination of deep learning models against traditional time series models is necessary to establish the advantages and disadvantages of deep learning models. Accordingly, even though deep learning models appear to exhibit a promising trend in time series prediction and analysis, a comprehensive evaluation is required. This combined approach of CNNs and LSTMs in the hybrid model has a higher chance of detecting true changes in stock price movements of the NSE market in real-time.