Stock Price Forecasting: Traditional Statistical Methods and Deep Learning Methods
DOI:
https://doi.org/10.54097/hbem.v21i.14754Keywords:
Stock Price Forecasting; ARIMA; LSTM.Abstract
Statistical method and machine learning methods can be applied to stock forecasting to assist users in making decisions in a variety of practical applications. This paper uses 50 past stock prices to predict future stock prices in this experiment. Both ARIMA and LSTM models are used in this study to predict the price. The close price on transaction days makes up the dataset in this study. The performance of two models is evaluated by MAE, MSE, and RMSE after running them in this research. And the result shows that both the LSTM model and ARIMA model predict the stock price well. To be specific, MSE values of ARIMA model are 1.17151, 1.55678, 6.38663 and 159.58281. MSE values of LSTM model are 1.18464, 1.07799, 4.87162 and 97.59937. In these two kinds of methods, LSTM model has a better performance than ARIMA model.
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