Abstract
LSTM (long-short term memory) Networks is one of the RNNs(recurrent neural networks). The algorithm was first published at Neural Computation by Sepp Hochreiter and Jurgen Schmidhuber. It performs better than normal RNNs in processing and predicting time series related data. At present, LSTM has achieved considerable success on many issues and has been widely used. Based on the excellent performance of LSTM Networks in time series, this article seeks to investigate whether LSTM can be applied to the stock price forecast. This paper compares the pros and cons of LSTM in time series prediction by comparing RNNs with LSTM. In this paper, the daily data of the Shanghai Composite Index and the Dow Jones Index is taken as the research object, and RNNs and LSTM are respectively used to construct the model. The criterion of the pros and cons of the model is the mean square error between predicted value and real value. This paper finally finds that LSTM can be well used in stock price forecasting.
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Acknowledgements
This research accepts the following grants: Chengdu Soft Science Research Fund (Project No.: 2015-RK00-00087-ZF; 2015-RK00-00257-ZF); Research Center Fund of Sichuan County Economic Development, Sichuan Provincial Key Research Center for Social Sciences (Project No.: XYJJ1506); Basic Scientific Research Fund of Sichuan University (Project No.: skqx2015-zx04; skzx2015-sb68; skyb201402); Humanities and Social Sciences Fund of Chinese Ministry of Education (Project No. 14YJC790053).
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Jiang, Q., Tang, C., Chen, C., Wang, X., Huang, Q. (2019). Stock Price Forecast Based on LSTM Neural Network. In: Xu, J., Cooke, F., Gen, M., Ahmed, S. (eds) Proceedings of the Twelfth International Conference on Management Science and Engineering Management. ICMSEM 2018. Lecture Notes on Multidisciplinary Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-93351-1_32
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DOI: https://doi.org/10.1007/978-3-319-93351-1_32
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