Stock price prediction based on SVM, LSTM, ARIMA
DOI:
https://doi.org/10.54691/bcpbm.v35i.3302Keywords:
SVM; LSTM; ARIMA; Machine Learning.Abstract
In general, forecasting on stock prices is a famous and interesting area that gathers many researchers in. Contemporarily, after the birth of AI, the number of the algorithms used in the prediction of equity market fluctuation are boomed rapidly. Applying the combination of statistics and algorithms can help researchers as well as investors learn about either short-term regulation (such as opening price) or the long-term market movement. This paper discusses three kinds of models which are used to predict the stock price for long or short term. Specifically, some empirical results are presented to prove the feasibility and significance of the models. By analyzing techniques used to predict stock prices and the limitation of these models, the discussion about the challenges and the outlook posed from the scope of future work in this filed are also shown and demonstrated. These results shed light on guiding further exploration of price forecasting for different kinds of underlying assets as well as portfolios.
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