Skip to main content
SpringerLink
Log in

A hybrid stock price index forecasting model based on variational mode decomposition and LSTM network

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Changes in the composite stock price index are a barometer of social and economic development. To improve the accuracy of stock price index prediction, this paper introduces a new hybrid model, VMD-LSTM, that combines variational mode decomposition (VMD) and a long short-term memory (LSTM) network. The proposed model is based on decomposition-and-ensemble framework. VMD is a data-processing technique through which the original complex series can be decomposed into a limited number of subseries with relatively simple modes of fluctuations. It can effectively overcome the shortcomings of mode mixing that sometimes exist in the empirical mode decomposition (EMD) method. LSTM is an improved version of recurrent neural networks (RNNs) that introduces a “gate” mechanism, and can effectively filter out the critical previous information, making it suitable for the financial time series forecasting. The capability of VMD-LSTM in stock price index forecasting is verified comprehensively by comparing with some single models and the EMD-based and other VMD-based hybrid models. Evaluated by level and directional prediction criteria, as well as a newly introduced statistic called the complexity-invariant distance (CID), the VMD-LSTM model shows an outstanding performance in stock price index forecasting. The hybrid models perform significantly better than the single models, and the forecasting accuracy of the VMD-based models is generally higher than that of the EMD-based models.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Nam K, Seong N (2019) Financial news-based stock movement prediction using causality analysis of influence in the Korean stock market. Decis Support Syst 117:100–112

    Google Scholar 

  2. Song Y, Lee J, Lee J (2019) A study on novel filtering and relationship between input-features and target-vectors in a deep learning model for stock price prediction. Appl Intell 49:897–911

    Google Scholar 

  3. J. Wang, J. Wang, Forecasting stock market indexes using principle component analysis and stochastic time effective neural networks, Neurocomputing156 (2015) 68–78

  4. Zhang C, Pan H, Ma Y, Huang X (2019) Analysis of Asia Pacific stock markets with a novel multiscale model. Physica A 534:120939

    Google Scholar 

  5. Loureiro ALD, Miguéis VL, Da SLFM (2018) Exploring the use of deep neural networks for sales forecasting in fashion retail. Decis Support Syst 114:81–93

    Google Scholar 

  6. Herzog S, Tetzlaff C, Wörgötter F (2020) Evolving artificial neural networks with feedback. Neural Netw 123:153–162

    Google Scholar 

  7. Adhikari R, Agrawal RK (2014) A combination of artificial neural network and random walk models for financial time series forecasting. Neural Comput Appl 24:1441–1449

    Google Scholar 

  8. Kuremoto T, Kimura S, Kobayashi K, Obayashi M (2014) Time series forecasting using a deep belief network with restricted boltzmann machines. Neurocomputing 137:47–56

    Google Scholar 

  9. Niu H, Wang J (2014) Financial time series prediction by a random data-time effective RBF neural network. Soft Comput 18:497–508

    Google Scholar 

  10. Wang J, Wang J (2017) Forecasting stochastic neural network based on financial empirical mode decomposition. Neural Netw 90:8–20

    Google Scholar 

  11. Das SP, Achary NS, Padhy S (2016) Novel hybrid SVM-TLBO forecasting model incorporating dimensionality reduction techniques. Appl Intell 45:1148–1165

    Google Scholar 

  12. I. Goodfellow, Y. Bengio, A. Courville, Deep learning: Cambridg, MIT press. 2016

    MATH  Google Scholar 

  13. Mandic DP, Chambers JA (1999) Exploiting inherent relationships in RNN architectures. Neural Netw 12:1341–1345

    Google Scholar 

  14. Deng T, He X, Zeng Z (2018) Recurrent neural network for combined economic and emission dispatch, applied intelligence. Appl Intell 48:2180–2198

    Google Scholar 

  15. Guo T, Xu Z, Yao X, Chen H, Aberer K, Funaya K (2016) Robust online time series prediction with recurrent neural networks, in: IEEE international conference on data science and advanced analytics. IEEE.:816–825

  16. Hajiabotorabi Z, Kazemi A, Samavati FF, Ghaini FMM (2019) Improving DWT-RNN model via B-spline wavelet multiresolution to forecast a high-frequency time series. Expert Syst Appl 138:112842

    Google Scholar 

  17. Wang Q, Xu W, Huang X, Yang K (2019) Enhancing intraday stock price manipulation detection by leveraging recurrent neural networks with ensemble learning. Neurocomputing 47:46–58

    Google Scholar 

  18. Berradi Z, Lazaar M (2019) Integration of principal component analysis and recurrent neural network to forecast the stock Price of Casablanca stock exchange. Procedia Computer Science 148:55–61

    Google Scholar 

  19. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–1780

    Google Scholar 

  20. Sundermeyer M, Ney H, Schlüter R (2015) From feedforward to recurrent LSTM neural networks for language modeling. IEEE/ACM Trans Audio Speech Lang Process 23:517–529

    Google Scholar 

  21. Mahmoudi N, Docherty P, Moscato P (2018) Deep neural networks understand investors better[J]. Decis Support Syst 112:23–34

    Google Scholar 

  22. Kraus M, Feuerriegel S (2017) Decision support from financial disclosures with deep neural networks and transfer learning[J]. Decis Support Syst 104:38–48

    Google Scholar 

  23. Vo NNY, He X, Liu S, Xu G (2019) Deep learning for decision making and the optimization of socially responsible investments and portfolio. Decis Support Syst 124:113097

    Google Scholar 

  24. Wang K, Qi X, Liu H (2019) Photovoltaic power forecasting based LSTM-convolutional network. Energy 189:116225

    Google Scholar 

  25. Karevan Z, Suykens JAK (2020) Transductive LSTM for time-series prediction: an application to weather forecasting. Neural Netw 125:1–9

    Google Scholar 

  26. Ding G, Qin L (2019) Study on the prediction of stock price based on the associated network model of LSTM. Int J Mach Learn & Cyber 11:1307–1317. https://doi.org/10.1007/s13042-019-01041-1

    Article  Google Scholar 

  27. S. Chen, L. Ge, Exploring the attention mechanism in LSTM-based Hong Kong stock price movement prediction, Quantitative Finance19 (2019) 1507–1515

  28. Baek Y, Kim HY (2018) ModAugNet: a new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module. Expert Syst Appl 113:457–480

    Google Scholar 

  29. Liang X, Ge Z, Sun L et al (2019) LSTM with wavelet transform based data preprocessing for stock Price prediction. Math Probl Eng 2019:1340174

    Google Scholar 

  30. Xu P, Xie F, Su T, Wan Z, Zhou Z, Xin X, Guan Z (2020) Automatic evaluation of facial nerve paralysis by dual-path LSTM with deep differentiated network. Neurocomputing 388:70–77. https://doi.org/10.1016/j.neucom.2020.01.014

    Article  Google Scholar 

  31. B. Zhang, H. Zhang, G. Zhao, J. Lian, Constructing a PM2.5 concentration prediction model by combining auto-encoder with Bi-LSTM neural networks, Environmental Modelling & Software 124 (2020) 104600

  32. Wang H, Yu L, Tian S, Peng YF, Pei XJ (2019) Bidirectional LSTM malicious webpages detection algorithm based on convolutional neural network and independent recurrent neural network. Appl Intell 49:3016–3026

    Google Scholar 

  33. Kim T, Kim HY (2019) Forecasting stock prices with a feature fusion LSTM-CNN model using different representations of the same data. PLoS One 14:e0212320

    Google Scholar 

  34. Kim HY, Won CH (2018) Forecasting the volatility of stock price index: a hybrid model integrating LSTM with multiple GARCH-type models. Expert Syst Appl 103:25–37

    Google Scholar 

  35. Yang D, Yang K (2016) Multi-step prediction of strong earthquake ground motions and seismic responses of SDOF systems based on EMD-ELM method. Soil Dyn Earthq Eng 85:117–129

    Google Scholar 

  36. N. Gu, H. Pan, Bearing fault diagnosis method based on EMD-CNNs, CSMA. (2017)

    Google Scholar 

  37. Xu W, Peng H, Zeng X, Zhou F, Tian X, Peng X (2019) A hybrid modelling method for time series forecasting based on a linear regression model and deep learning. Appl Intell 49:3002–3015

    Google Scholar 

  38. Yu L, Wang S (2008) Kin Keung Lai, forecasting crude oil price with an EMD-based neural network ensemble learning paradigm. Energy Econ 30:2623–2635

    Google Scholar 

  39. Zhou LG, Fujita H (2017) Posterior probability based ensemble strategy using optimizing decision directed acyclic graph for multi-class classification. Inf Sci 400-401:142–156

    Google Scholar 

  40. Sun J, Li H, Fujita H, Fu BB, Ai WG (2020) Class-imbalanced dynamic financial distress prediction based on Adaboost-SVM ensemble combined with SMOTE and time weighting. Information Fusion 54:128–144

    Google Scholar 

  41. J. Sun, H. Fujita, P. Chen, H. Li, Dynamic financial distress prediction with concept drift based on time weighting combined with Adaboost support vector machine ensemble, Knowledge-Based Systems120 (2017) 4–14

  42. Cao J, Li Z, Li J (2019) Financial time series forecasting model based on CEEMDAN and LSTM. Physica A 519:127–139

    Google Scholar 

  43. Wang J, Tang L, Luo Y, Ge P (2017) A weighted EMD-based prediction model based on TOPSIS and feed forward neural network for noised time series. Knowl-Based Syst 132:167–178

    Google Scholar 

  44. Huang NE, Shen Z, Long SR et al (1988) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings: mathematical. Phys Eng Sci 454:903–995

    MATH  Google Scholar 

  45. Awajan AM, Ismail MT, al Wadi S (2018) Improving forecasting accuracy for stock market data using EMD-HW bagging. PLoS One 13:e0199582

    Google Scholar 

  46. Li H (2017) Price forecasting of stock index futures based on a new hybrid EMD-RBF neural network model. Agro Food Industry Hi Tech 28:1744–1747

    Google Scholar 

  47. Yang H, Lin H (2016) An integrated model combined ARIMA, EMD with SVR for stock indices forecasting. International Journal on Artificial Intelligence Tools 25:1650005

    Google Scholar 

  48. Zhang B, Wang J, Fang W (2015) Volatility behavior of visibility graph EMD financial time series from Ising interacting system. Physica A 432:301–314

    Google Scholar 

  49. Huang Y, Hou S, Xu S, Zhao S, Yang L, Zhang Z (2019) EMD-PNN based welding defects detection using laser-induced plasma electrical signals. J Manuf Process 45:642–651

    Google Scholar 

  50. Tang L, Wu Y, Yu L (2018) A randomized-algorithm-based decomposition-ensemble learning methodology for energy price forecasting. Energy 157:526–538

    Google Scholar 

  51. Upadhyay A, Pachori RB (2015) Instantaneous voiced/non-voiced detection in speech signals based on variational mode decomposition. J Frankl Inst 352:2679–2707

    MATH  Google Scholar 

  52. Liu C, Cheng G, Chen X, Pang Y (2018) Planetary gears feature extraction and fault diagnosis method based on VMD and CNN. Sensors 18:1523

    Google Scholar 

  53. Lahmiri S (2014) Comparative study of ECG signal denoising by wavelet thresholding in empirical and variational mode decomposition domains. Healthc Technol Lett 1:104–109

    Google Scholar 

  54. Lahmiri S (2016) A variational mode decomposition approach for analysis and forecasting of economic and financial time series. Expert Syst Appl 55:268–273

    Google Scholar 

  55. Abdoos AA (2016) A new intelligent method based on combination of VMD and ELM for short term wind power forecasting. Neurocomputing 203:111–120

    Google Scholar 

  56. Shahzad SJH, Nor SM, Kumar RR, Mensi W (2017) Interdependence and contagion among industry-level US credit markets: an application of wavelet and VMD based copula approaches. Physica A 466:310–324

    Google Scholar 

  57. Bisoi R, Dash PK, Parida AK (2019) Hybrid Variational mode decomposition and evolutionary robust kernel extreme learning machine for stock price and movement prediction on daily basis. Appl Soft Comput 74:652–678

    Google Scholar 

  58. Salim L (2016) Intraday stock price forecasting based on variational mode decomposition. Journal of Computational Science 12(2016):23–27

    Google Scholar 

  59. Liu Y, Yang C, Huang K, Gui W (2019) Non-ferrous metals price forecasting based on variational mode decomposition and LSTM network. Knowl-Based Syst 188:105006. https://doi.org/10.1016/j.knosys.2019.105006

    Article  Google Scholar 

  60. Liu H, Mi X, Li Y (2018) Smart multi-step deep learning model for wind speed forecasting based on variational mode decomposition, singular spectrum analysis. LSTM network and ELM, Energy Conversion and Management 159:54–64

    Google Scholar 

  61. Dragomiretskiy K, Zosso D (2014) Variational mode decomposition. IEEE Trans Signal Process 62:531–544

    MathSciNet  MATH  Google Scholar 

  62. Boyd S, Parikh N, Chu E et al (2011) Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine Learning 3:1–122

    MATH  Google Scholar 

  63. Makridakis S (1993) Accuracy measures: theoretical and practical concerns. Int J Forecasting 9:527–529

    Google Scholar 

  64. G.E.A.P.A. Batista, E.J. Keogh, O.M. Tataw, V.M. Alves de Souza, CID: an efficient complexity-invariant distance for time series, Data Mining and Knowledge Discovery 28 (2014) 634–669

Download references

Acknowledgments

The work was partially supported by the Humanities and Social Sciences Foundation of Ministry of Education of China (No. 18YJCZH134, 18YJC790106) and the Fundamental Research Funds for the Central Universities (No. FRF-BR-18-001B).

Author information

Authors and Affiliations

  1. Donlinks School of Economics and Management, University of Science and Technology Beijing, Beijing, 100083, China

    Hongli Niu, Kunliang Xu & Weiqing Wang

Authors
  1. Hongli Niu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kunliang Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Weiqing Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kunliang Xu.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Niu, H., Xu, K. & Wang, W. A hybrid stock price index forecasting model based on variational mode decomposition and LSTM network. Appl Intell 50, 4296–4309 (2020). https://doi.org/10.1007/s10489-020-01814-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-020-01814-0

Keywords

Search

Navigation