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Research on a hybrid prediction model for stock price based on long short-term memory and variational mode decomposition

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Abstract

The stock market plays a vital role in the economic and social organization of many countries. Since stock price time series are highly noisy, nonparametric, volatility, complexity, nonlinearity, dynamics, and chaos, the stock market prediction is an important issue for investors and professional analysts. In the financial field, stock market prediction is not only an important task but also an important research topic. For different problems, researchers have proposed many prediction methods. Many papers provide strong evidence that stock prices can be predicted from past price data. In this paper, we propose a hybrid prediction model for stock price based on long short-term memory (LSTM) and variational mode decomposition (VMD). We use the variational mode decomposition method to decompose the complex time series of stock prices into several relatively flat, regular, and stable subsequences. Then, we use each subsequence to train the long- and short-term memory neural network and predict each subsequence. Finally, we merge the predicted values of several subsequences to form the predicted results of the stock price complex original time series. To verify fully the method, we selected four experimental data for testing. Compared with the prediction results of various prediction methods, the prediction accuracy of our proposed model is higher. Especially in the R2 index, the experimental effect is very good. The proposed method achieves good results of more than 0.991 on each data set. Therefore, our proposed hybrid prediction model is accurate and effective in forecasting stock prices and has practical significance and reference value.

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Data Availability

Data are fully available without restriction. The original experimental data can be downloaded from Yahoo Finance for free(http://finance.yahoo.com).

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Acknowledgements

This work was supported in part by the Scientific Research Fund of Hunan Provincial Education under Grants 20C1487 and 19C1472, Research results of Educational Science Planning in Hunan Province under Grant XJ212259, the Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province under Grants ZNKZD2020-1 and ZNKZ2018-5, Key scientific research projects of Huaihua University under Grant HHUY2019-08, Project of Huaihua City Social Science Achievement Review Committee under Grant HSP2021YB101 and HSP2021YB102, Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Huaihua University, Huaihua, 418008, China

    Yang Yujun & Yang Yimei

  2. School of Computer and Software Engineering, Xihua University, Chengdu, 610039, China

    Zhou Wang

  3. Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities, Huaihua, 418000, China

    Yang Yujun & Yang Yimei

  4. Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province, Huaihua, 418000, China

    Yang Yujun

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  1. Yang Yujun
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  3. Zhou Wang
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Yang Yujun contributed to all aspects of this work. Yang Yimei and Zhou Wang conducted the experiment and analyzed the data. All authors reviewed the manuscript.

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Correspondence to Yang Yimei.

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Yujun, Y., Yimei, Y. & Wang, Z. Research on a hybrid prediction model for stock price based on long short-term memory and variational mode decomposition. Soft Comput 25, 13513–13531 (2021). https://doi.org/10.1007/s00500-021-06122-4

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