Paper Contents
Abstract
Stocks represent a form of security that provides shareholders with a degree of ownership in a corporation, along with rights to the company's profits and assets. As a result of the company's changing valuation, stock prices also vary, making the prediction of stock values a difficult undertaking due to the volatile and nonlinear characteristics of financial markets. This is where Advanced Machine Learning comes into play; Machine Learning, which is a branch of Artificial Intelligence, employs algorithms to enable computers to learn from data without being explicitly programmed. In traditional approaches, the model is often overwhelmed by noise or hindered by rigid labeling methods. So by using Machine Learning algorithms and techniques, stock price predictions become more accessible and accurate, as they can identify patterns and trends in financial data. In order to fix the issue of noise, there are various labeling methods to be found in Machine Learning, including but not limited to N-Period Min-Max labeling, along with advanced techniques like Variational Mode Decomposition, Model-agnostic meta-learning, and Long short-term memory. These approaches work together to enhance the precision of stock predictions in complex financial markets. This study presents a new framework for predicting stock prices that tries to turn these very challenges into opportunities. The study aims to provide reliable, interpretable stock price forecasts
Copyright
Copyright © 2024 P. Prudhvi Raj. This is an open access article distributed under the Creative Commons Attribution License.
