Stock price prediction based on SVM, LSTM, ARIMA

In general, forecasting on stock prices is a famous and interesting area that gathers many researchers in. Contemporarily, after the birth of AI, number algorithms used in prediction equity market fluctuation are boomed rapidly. Applying combination statistics can help as well investors learn about either short-term regulation (such opening price) or long-term movement. This paper discusses three kinds models which to predict price for long short term. Specifically, some empirical results presented prove feasibility significance models. By analyzing techniques limitation these models, discussion challenges outlook posed from scope future work this filed also shown demonstrated. These shed light guiding further exploration different underlying assets portfolios.