Stock Prediction based on Bayesian-LSTM

Biao Huang · Qiao Ding · Guozi Sun · Huakang Li

36 citations0/0 files storedInternational Conference on Machine Learning and Computing (ICMLC)

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Stock Market Forecasting Methods Hydrological Forecasting Using AI Time Series Analysis and Forecasting

Abstract

Fluctuations in stock market represent the changes in national economic objectively. Some machine learning algorithms, such as linear fitting and sequence mining, are employed to predict the stock market. However, linear fitting faces issue of over-fitting and black relationships with historical data, while sequence mining is short in efficiency and lack dynamic adaptations. This paper builds a modified Bayesian-LSTM (B-LSTM) model for stock prediction. Six indicators of the Chinese stock market in every day are the basic input for LSTM. In order to represent the economic wave, we defined a data set unit by week which means the basic unit in LSTM is data in one week. Furthermore, a Bayesian optimization model is proposed to estimate the unit number dynamically in different economic cycle. The experimental results demonstrated that the B-LSTM increase over 25% prediction than the conventional LSTM.

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References

1996Stock Market Development and Long-Run Growth 2015A LSTM-based method for stock returns prediction: A case study of China stock market 2016Deep learning for stock prediction using numerical and textual information 1996Introduction to financial forecasting 2008Practical Bayesian optimization 1990Stock price prediction using neural networks: A project report 1997Stock Price Prediction Using Prior Knowledge and Neural Networks 2015Stock price prediction using linear regression based on sentiment analysis

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