Enhancing Stock Market Predictions: Leveraging Machine Learning and Time Series Analysis for Accurate Forecasting

Abstract

The practice of projecting the future value of stocks traded on the stock market in order to generate profits is known as stock price prediction using machine learning. It is difficult to predict stock prices with great accuracy because there are so many variables involved; here is where machine learning is essential. Stock prices for the upcoming day or week can be predicted by treating stock data as a time series and use historical stock prices together with additional criteria. Popular machine learning models used to predict time series data, including weather forecasts, election results, home prices, and of course stock prices, are Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM). The objective is to ascertain which criteria have the greatest influence on the prices for the "current" or "next" day by weighing the significance of recent and older data. In order to anticipate future stock values, a machine learning model weighs each market function and decides which historical data points to use.