E3S Web Conf.
Volume 218, 20202020 International Symposium on Energy, Environmental Science and Engineering (ISEESE 2020)
|Number of page(s)||5|
|Section||Research on Energy Technology Application and Consumption Structure|
|Published online||11 December 2020|
Comparison of ARIMA, ANN and LSTM for Stock Price Prediction
Sun Yat-sen University. Guangzhou, Guangdong, P. R. China
* Corresponding author: firstname.lastname@example.org
The prediction of stock prices has always been a hot topic of research. However, the autoregressive integrated moving average (ARIMA) model commonly used and artificial neural networks (ANN) still have their own advantages and disadvantages. The use of long short-term memory (LSTM) networks model for prediction also shows interesting possibilities. This article compares three models specifically through the analysis of the principles of the three models and the prediction results. In the end, it is believed that the LSTM model may have the best predictive ability, but it is greatly affected by the data processing. The ANN model performs better than that of the ARIMA model. The combination of time series and external factors may be a worthy research direction.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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