Comparison of Machine Learning Models for Stock Prediction

The stock market, a significant component of the financial market, is essential to the functioning of the world economy. Accurate prediction of changes in stock prices is of great importance to investors, financial institutions, and the economic system. In order to compare the effects of three methods-linear regression, K-nearest neighbor (KNN), and long short-term memory network (LSTM)-in the context of Tesla stock prediction, the goal of this study is to investigate the use of machine learning in the field of stock prediction. Through empirical analysis and comprehensive evaluation, this paper finds that the LSTM model performs best in Tesla stock prediction, with better prediction accuracy and stability. LSTM can better capture the time series characteristics and complex nonlinear relationships of stock prices, thus improving the accuracy of prediction. This research investigates the future development direction of machine learning techniques in stock forecasting, building upon the discovered insights. Subsequent investigations may concentrate on broadening the scope of data attributes, investigating group education techniques, and including attention mechanisms. The explorations and innovations will provide investors with more reliable and accurate decision support and help them make more informed investment decisions in the stock market, to achieve better investment returns. By comprehensively evaluating and comparing the performance of different machine learning methods, this paper can provide useful references and guidance for research and practice in the field of stock prediction.