Stock Price Forecast Based On EMD-PCA-GRU Neural Network Model

DOI

10.2991/978-94-6463-042-8_139How to use a DOI?

Keywords

stock price forecasting; empirical mode decompos-ition; principal component analysis; gated recurrent unit; deep learning

Abstract

In order to further improve the accuracy of stock price prediction, this paper combines EMD, PCA and GRU to build a financial time series data prediction model. Firstly, the daily degree and 5-minute closing price of the CSI300 index were substituted into EMD for data denoising, and the decomposed IMF components were substituted into PCA to reduce the dimension of the data, so as to improve the prediction accuracy and efficiency of the model. Finally, the extracted principal components were substituted into GRU model for stock price prediction. The results show that the prediction accuracy of EMD-PCA-GRU model is better than other models. And it is proved that the composite model has a higher degree of fit than the single model. Under the same model, the prediction accuracy of 5 minutes high frequency time series is better.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Meijuan YANG
PY  - 2022
DA  - 2022/12/29
TI  - Stock Price Forecast Based On EMD-PCA-GRU Neural Network Model
BT  - Proceedings of the 2022 International Conference on mathematical statistics and economic analysis (MSEA 2022)
PB  - Atlantis Press
SP  - 976
EP  - 981
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-042-8_139
DO  - 10.2991/978-94-6463-042-8_139
ID  - YANG2022
ER  -