SHS Web Conf.
Volume 170, 20232023 International Conference on Digital Economy and Management Science (CDEMS 2023)
|Number of page(s)||4|
|Section||Economic Innovation and Talent Development Technology|
|Published online||14 June 2023|
Apply RF-LSTM to Predicting Future Share Price
The college of communication engineering, Jilin University, 130012, China
* Corresponding author: email@example.com
In recent years, share has become a more and more heated topic in the whole world. As one of the most significant items, the data is featured by great complexity, especially in price prediction. According to the previous relevant study, its prediction has high requirements for the model, which indicates using a single model cannot acquire relatively accurate prediction results. With regard to this problem, integrating random forest and long short-term memory is illustrated to solve that. The first step is the normalization of related share data, which is to reduce the influence caused by the discrepancy of different data. And then, random forest is used for choosing relatively optimal feature rally. In contrast to single decision tree, the application of random forest has simplified the complexity of training. After that, long short-term memory is used for forecasting the price and optimize plentiful important parameters in the model. According to test consequence, the error rate of the integrated model is decreased obviously.
© The Authors, published by EDP Sciences, 2023
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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