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MAE, MSE and RMSE performance indicators are used to analyze the performanceof different stocks predicted by LSTM and ARIMA models in this paper. 50 listedcompany stocks from finance.yahoo.com are selected as the research object inthe experiments. The dataset used in this work consists of the highest price ontransaction days, corresponding to the period from 01 January 2010 to 31December 2018. For LSTM model, the data from 01 January 2010 to 31 December2015 are selected as the training set, the data from 01 January 2016 to 31December 2017 as the validation set and the data from 01 January 2018 to 31December 2018 as the test set. In term of ARIMA model, the data from 01 January2016 to 31 December 2017 are selected as the training set, and the data from 01January 2018 to 31 December 2018 as the test set. For both models, 60 days ofdata are used to predict the next day. After analysis, it is suggested thatboth ARIMA and LSTM models can predict stock prices, and the prediction resultsare generally consistent with the actual results;and LSTM has betterperformance in predicting stock prices(especially in expressing stock pricechanges), while the application of ARIMA is more convenient.

Predict stock prices with ARIMA and LSTM