期刊名称：International Journal of Computer Science Issues
印刷版ISSN：1694-0784
电子版ISSN：1694-0814
出版年度：2019
卷号：16
期号：3
页码：27-31
DOI：10.5281/zenodo.3252969
出版社：IJCSI Press
摘要：Stock market prediction has always been crucial for stakeholders, traders and investors. We developed an ensemble Long Short Term Memory (LSTM) model that includes two time frequencies (annual and daily parameters) in order to predict next day Closing price (one-step ahead). Based on a four-step approach, this methodology is a serial combination of two LSTM algorithms. The empirical experiment is applied to 417 NY stock exchange companies. Based on Open High Low Close metrics and other financial ratios, the approach proves that the stock market prediction can be improved.
关键词：Times series forecasting; Prediction model; Long;Short Term Memory; Deep Learning
