Stock Market Forecasting
This study forecasts the closing prices of numerous corporations using Long-Short Term Memory (LSTM) methodologies and finds that LSTM works better than SVR, as shown by the experiment's findings.
Abstract
Abstract: Stock market forecasting seeks to determine the worth of a firm’s financial stocks in the future. Machine learning is being used in recent developments in stock market forecasting technology to produce forecasts based on the values of current stock market indices by training on their previous values. Future stock price projections can be difficult to make when trying to anticipate the stock market. It is incredibly challenging to forecast the stock market since shares fluctuate so frequently. Every day and frequently, stock. Foreseeing trends in the stock market is often correct using this method. This study forecasts the closing prices of numerous corporations using Long-Short Term Memory (LSTM) methodologies. These models are assessed using RMSE, which is one of the commonly used error measures. LSTM works better than SVR, as shown by the experiment's findings