AI-Powered Forecasting for Indian Stock Markets

Abstract

In the rapidly evolving financial markets, accurately predicting stock prices is crucial for investors seeking to optimize their portfolios and mitigate risks. This project leverages machine learning techniques to develop a predictive model for stock price forecasting. We utilize historical stock price data, along with relevant economic indicators and market sentiment, to construct a robust dataset. Key methodologies include time series analysis, regression models, and advanced machine learning algorithms, such as Long Short-Term Memory (LSTM) networks, which excel at capturing temporal dependencies in sequential data. The project involves comprehensive data preprocessing and feature engineering to enhance model performance. Various models are trained and evaluated using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) to ensure accuracy and reliability. The results demonstrate the potential of machine learning approaches in financial predictions, revealing patterns and insights that can inform investment strategies. This research aims to contribute to the field of predictive analytics in finance, offering a framework for future studies and practical applications in stock market forecasting. cognitive radio(CR) is a transceiver which automatically detects available channels in wireless spectrum and accordingly changes its transmission or reception parameters. In this paper, it proposes an algorithm for the energy-efficient and spectrum- aware communications requirements in CR network. It enables each node to determine and regulate its transmission strategy to provide minimum energy consumption without sacrificing end-to-end delay performance and also maximizes overall spectrum utilization. Spectrum sensing is one of the essential parameter to be considered in CR networks. Therefore, the security aspect of spectrum sensing should be addressed well. Using a Trust-Worthy algorithm, it improves the trustworthiness of the Spectrum sensing in CR-Networks. It implemented using Network Simulator-2.