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.
翻译:使用MAE、MSE和RMSE业绩指标来分析LSTM和ARIMA模型预测的不同库存绩效。50个来自Finance.yahoo.com的上市公司库存被选为实验中的研究对象。这项工作使用的数据集包括交易日最高价格,相当于2010年1月1日至2018年12月31日。LSTM模型中,2010年1月1日至2015年12月31日的数据被选为培训数据集,2016年1月1日至2017年12月31日的数据作为验证数据集,201818年1月1日至2018年12月31日的数据作为测试集。在ARIMA模型中,2016年1月1日至201717年12月31日的数据被选为培训对象,2018年1月1日至2018年12月31日的数据作为测试集。两种模型中,60天的数据被用来预测第二天的数据。在分析后,建议ARIMA和LSTM模型可以预测股票价格,预测结果一般与实际结果一致;LSTM在预测股票价格方面业绩方面(特别是在表示股票价格变动方面)。