  MAE, MSE and RMSE performance indicators are used to analyze the performance
of different stocks predicted by LSTM and ARIMA models in this paper. 50 listed
company stocks from finance.yahoo.com are selected as the research object in
the experiments. The dataset used in this work consists of the highest price on
transaction days, corresponding to the period from 01 January 2010 to 31
December 2018. For LSTM model, the data from 01 January 2010 to 31 December
2015 are selected as the training set, the data from 01 January 2016 to 31
December 2017 as the validation set and the data from 01 January 2018 to 31
December 2018 as the test set. In term of ARIMA model, the data from 01 January
2016 to 31 December 2017 are selected as the training set, and the data from 01
January 2018 to 31 December 2018 as the test set. For both models, 60 days of
data are used to predict the next day. After analysis, it is suggested that
both ARIMA and LSTM models can predict stock prices, and the prediction results
are generally consistent with the actual results;and LSTM has better
performance in predicting stock prices(especially in expressing stock price
changes), while the application of ARIMA is more convenient.
