Enhancing Stock Market Prediction with ARIMA and Machine Learning

Abstract

ML and ARIMA-based stock market forecasting. Brokers, derivatives, currencies, and stocks are used in the vast and convoluted structure of the financial markets, which is always changing and evolving. Compared to the hazards of beginning a new business or the requirement for a high-paying employment, this market provides investors the potential to make money and live a happy life with a minimal initial investment. However, assessing and managing the performance of machine learning requires human-assessed risk management procedures and security precautions. For this research, it is important to predict stock prices using ARIMA and machine learning techniques. With the aid of machine learning and the ARIMA model, stock values may be forecasted with simplicity. This includes a range of work that was done on the review paper using different learning techniques. The most notable features are ARIMA and the built-in machine learning. Oblivion Gate removes data that doesn't match the algorithm, leaving only data that does. As soon as information enters the network, rules enable selection. Three gate structures combine to produce a single network structure.