Abstract
In order to test the predictive power of the deep learning model, several machine learning methods were introduced for comparison. Empirical case results for the period of 2000 to 2017 show the forecasting power of deep learning technology. With a series of linear regression indicator measurement, we find LSTM networks outperform traditional machine learning methods, i.e., Linear Regression, Auto ARIMA, KNN.
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Acknowledgments
This research was partially supported by National Natural Science Foundation of China (Grant No. 71771006 and 71771008) and the Fundamental Research Funds for the Central Universities.
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Lin, Y., Liu, S., Yang, H., Wu, H. (2020). A Deep Learning Framework for Stock Prediction Using LSTM. In: Bucciarelli, E., Chen, SH., Corchado, J. (eds) Decision Economics: Complexity of Decisions and Decisions for Complexity. DECON 2019. Advances in Intelligent Systems and Computing, vol 1009. Springer, Cham. https://doi.org/10.1007/978-3-030-38227-8_8
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