Github Asimkymk Stock Market Trend Forecasting Using Explainable

Github Asimkymk Stock Market Trend Forecasting Using Explainable In this project, we propose a comprehensive framework for stock market trend forecasting that incorporates multi factor analysis and explainable artificial intelligence. Aims to develop a comprehensive framework for predicting stock market trends by combining traditional time series analysis with multi factor analysis (google trend values and daily news scores) from external data sources.

Github Ankitamullagiri Stock Market Forecasting Aims to develop a comprehensive framework for predicting stock market trends by combining traditional time series analysis with multi factor analysis (google trend values and daily news scores) from external data sources. In this project, we propose a comprehensive framework for stock market trend forecasting that incorporates multi factor analysis and explainable artificial intelligence. Aims to develop a comprehensive framework for predicting stock market trends by combining traditional time series analysis with multi factor analysis (google trend values and daily news scores) fro…. Our study shows that the proposed deep learning model, enhanced with explainable ai (xai) techniques, provides a robust and interpretable method for predicting multi class stock market trends.

Github Rjptpradeep Building A Stock Trend Forecasting Using Lstm In Aims to develop a comprehensive framework for predicting stock market trends by combining traditional time series analysis with multi factor analysis (google trend values and daily news scores) fro…. Our study shows that the proposed deep learning model, enhanced with explainable ai (xai) techniques, provides a robust and interpretable method for predicting multi class stock market trends. This research provides a novel model that combines the catboost approach with the marine predators algorithm strategy to tackle many difficulties efficiently and demonstrates that the proposed model is a dependable as well as beneficial approach for generating time series data on stock prices. To tackle these issues, we propose our summarize explain predict (sep) framework, which utilizes a verbal self reflective agent and proximal policy optimization (ppo) that allow a llm teach itself how to generate explainable stock predictions, in a fully autonomous manner. This project implements a convolutional neural network (cnn) and long short term memory (lstm) model to predict stock prices. the model uses historical stock data, along with technical indicators, to forecast future stock prices. Predicting stock prices is not an easy job because the market keeps changing a lot and many outside things affect it. in this project, we are trying to build an explainable ai (xai) model that not only predicts stock prices but also explains how it came to those predictions.