Stock Price Prediction Using LSTM and SVM

This article's goal is to help investors choose between the support vector machine (SVM) and the long short-term memory (LSTM) models for forecasting stock prices. The suggested method takes into account both internal and external aspects in its prediction process, combining mathematical functions with machine learning approaches. The stock market may be traded in two basic ways. Both day trading (intraday) and long-term holding (intraday) are included. Because of its capacity to memorize crucial past data for use in predicting future stock values, LSTM models are well-suited to this purpose. Although accurate price forecasting is challenging, researchers are working to build algorithms that can anticipate whether prices will grow or decline. Due to the inherent volatility and complexity of the stock market, projecting stock prices is a difficult undertaking. Forecasts are meant to serve as a reference, not an absolute. Before making any financial commitments, investors should weigh the benefits of prediction models against their dangers and limits. The purpose of this article is to examine the strengths and weaknesses of SVM and LSTM models in terms of their ability to forecast stock prices.