Auto-ML Stock Prediction: A Rule based Model Approach to Forecast the Bull and Bearish Markets in Different Sectors

Abstract

In Stock Market Prediction, the main aim to predict the Sector Wise Model Selection to Forecast Based on Bullish & Bearish Condition of Share Market explores the development and implementation of a comprehensive framework for stock price prediction using ARIMA, SARIMA, and LSTM models. Multiple process of attempts to forecast future stock market behaviour, but due to its complexity, accurate predictions remain challenging. Machine learning techniques which have proven effective in this domain.

The primary aim is to forecast stock prices by analysing historical data, identifying trends, and evaluating model performance using various statistical and machine learning approaches. The study incorporates data from selected stocks over different time periods, applying logarithmic transformations and splitting data into training and validation sets to enhance model accuracy. Additionally, the project examines bullish and bearish trends separately to provide more granular insights. Performance metrics such as RMSE, MAPE, and MSLE are used to evaluate and compare model predictions.  The results demonstrate the potential of these models in capturing stock price movements and highlight areas for further refinement and integration of more advanced techniques.