Skip to main content
SpringerLink
Log in

VAR-GRU: A Hybrid Model for Multivariate Financial Time Series Prediction

  • Conference paper
  • First Online:
Intelligent Information and Database Systems (ACIIDS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12034))

Included in the following conference series:

Abstract

A determining the most relevant variables and proper lag length are the most challenging steps in multivariate time series analysis. In this paper, we propose a hybrid Vector Autoregressive and Gated Recurrent Unit (VAR-GRU) model to find the contextual variables and suitable lag length to improve the predictive performance for financial multivariate time series. VAR-GRU approach consists of two layers, the first layer is a VAR model-based variable and lag length selection and in the second layer, the GRU-based multivariate prediction model is trained. In the VAR layer, the Akaike Information Criterion (AIC) is used to select VAR order for finding the optimal lag length. Then, the Granger Causality test with the optimal lag length is utilized to define the causal variables to the second layer GRU model. The experimental results demonstrate that the ability of the proposed hybrid model to improve prediction performance against all base predictors in terms of three evaluation metrics. The model is validated over real-world financial multivariate time series dataset.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Beaver, W.H.: Market prices, financial ratios, and the prediction of failure. J. Account. Res. 179–192 (1968). https://doi.org/10.2307/2490233

    Article  Google Scholar 

  2. Cho, K., Merrienboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: Encoder–Decoder approaches. In: Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, pp. 103–111. Association for Computational Linguistics, Doha, Qatar (2014). https://doi.org/10.3115/v1/w14-4012

  3. Lütkepohl, H.: New Introduction to Multiple Time Series Analysis. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-27752-1

    Book  MATH  Google Scholar 

  4. Fischer, T., Krauss, C.: Deep learning with long short-term memory networks for financial market predictions. Eur. J. Oper. Res. 270(2), 654–669 (2018). https://doi.org/10.1016/j.ejor.2017.11.054

    Article  MathSciNet  MATH  Google Scholar 

  5. Ding, X., Zhang, Y., Liu, T., Duan, J.: Deep learning for event-driven stock prediction. In: Twenty-Fourth International Joint Conference on Artificial Intelligence, pp. 2327–2333. IJCAI, Buenos Aires, Argentina (2015)

    Google Scholar 

  6. Yang, E., et al.: A simulation-based study on the comparison of statistical and time series forecasting methods for early detection of infectious disease outbreaks. Int. J. Environ Health Res. 15(5), 966 (2018). https://doi.org/10.3390/ijerph15050966

    Article  Google Scholar 

  7. Wang, J., Wang, J., Fang, W., Niu, H.: Financial time series prediction using Elman recurrent random neural networks. Comput. Intell. Neurosci. 2014, 1–14 (2016). https://doi.org/10.1155/2016/4742515

    Article  Google Scholar 

  8. Rather, A.M., Agarwal, A., Sastry, V.N.: Recurrent neural network and a hybrid model for prediction of stock returns. Expert Syst. Appl. 42(6), 3234–3241 (2015). https://doi.org/10.1016/j.eswa.2014.12.003

    Article  Google Scholar 

  9. Munkhdalai, L., et al.: An end-to-end adaptive input selection with dynamic weights for forecasting multivariate time series. IEEE Access 7, 99099–99114 (2019). https://doi.org/10.1109/ACCESS.2019.2930069

    Article  Google Scholar 

  10. Reinsel, G.C.: Elements of Multivariate Time Series Analysis. Springer, New York (2003)

    MATH  Google Scholar 

  11. Jin, C.H., et al.: A SOM clustering pattern sequence-based next symbol prediction method for day-ahead direct electricity load and price forecasting. Energy Convers. Manag. 90, 84–92 (2015). https://doi.org/10.1016/j.enconman.2014.11.010

    Article  Google Scholar 

  12. Jin, C.H., Pok, G., Park, H.W., Ryu, K.H.: Improved pattern sequence-based forecasting method for electricity load. IEEJ Trans. Electr. Electron. Eng. 9(6), 670–674 (2014). https://doi.org/10.1002/tee.22024

    Article  Google Scholar 

  13. Islam, F., Shahbaz, M., Ahmed, A.U., Alam, M.M.: Financial development and energy consumption nexus in Malaysia: a multivariate time series analysis. Econ. Model. 30, 435–441 (2013). https://doi.org/10.1016/j.econmod.2012.09.033

    Article  Google Scholar 

  14. Zhang, G.P.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing. 50, 159–175 (2003). https://doi.org/10.1016/S0925-2312(01)00702-0

    Article  MATH  Google Scholar 

  15. Guresen, E., Kayakutlu, G., Daim, T.U.: Using artificial neural network models in stock market index prediction. Expert Syst. Appl. 38(8), 10389–10397 (2011). https://doi.org/10.1016/j.eswa.2011.02.068

    Article  Google Scholar 

  16. Zhong, X., Enke, D.: Forecasting daily stock market return using dimensionality reduction. Expert Syst. Appl. 67, 126–139 (2017). https://doi.org/10.1016/j.eswa.2016.09.027

    Article  Google Scholar 

  17. Atsalakis, G.S., Valavanis, K.P.: Surveying stock market forecasting techniques–Part II: soft computing methods. Expert Syst. Appl. 36(3), 5932–5941 (2009). https://doi.org/10.1016/j.eswa.2008.07.006

    Article  Google Scholar 

  18. Zivot, E., Wang, J.: Modeling Financial Time Series with S-Plus®. Springer, New York (2007). https://doi.org/10.1007/978-0-387-32348-0

    Book  MATH  Google Scholar 

  19. Akaike, H.: Fitting autoregressive models for prediction. Ann. Inst. Stat. Math. 21(1), 243–247 (1969)

    Article  MathSciNet  Google Scholar 

  20. Granger, C.W.: Investigating causal relations by econometric models and cross-spectral methods. Econometrica 424–438 (1969). https://doi.org/10.2307/1912791

    Article  Google Scholar 

  21. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003). https://doi.org/10.1162/153244303322753616

    Article  MATH  Google Scholar 

  22. Elman, J.L.: Finding structure in time. Cogn. Sci. 14(2), 179–211 (1990). https://doi.org/10.1207/s15516709cog1402_1

    Article  Google Scholar 

  23. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735

    Article  Google Scholar 

  24. Gulli, A., Pal, S.: Deep Learning with Keras. Packt Publishing Ltd., Birmingham (2017)

    Google Scholar 

  25. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). https://arxiv.org/abs/1412.6980

  26. Abadi, M., et al.: Tensorflow: a system for large-scale machine learning. In: 12th Symposium on Operating Systems Design and Implementation. pp. 265–283. USENIX, Savannah (2016)

    Google Scholar 

Download references

Acknowledgements

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No. 2017R1A2B4010826) and (No. 2019K2A9A2A06020672) in Republic of Korea, and by the National Natural Science Foundation of China (Grant No. 61702324 and Grant No. 61911540482) in People’s Republic of China.

Author information

Authors and Affiliations

  1. Database/Bioinformatics Laboratory, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju, 28644, Republic of Korea

    Lkhagvadorj Munkhdalai

  2. College of Information Engineering, Shanghai Maritime University, 213, 1550 Haigang Avenue Pudong New Area, Shanghai, China

    Meijing Li

  3. Department of Electrical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, 50200, Thailand

    Nipon Theera-Umpon

  4. Department of Computer Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, 50200, Thailand

    Sansanee Auephanwiriyakul

  5. Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, 700000, Vietnam

    Keun Ho Ryu

  6. Department of Computer Science, College of Electrical and Computer Engineering, Chungbuk National University, Cheongju, 28644, Republic of Korea

    Keun Ho Ryu

Authors
  1. Lkhagvadorj Munkhdalai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Meijing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nipon Theera-Umpon
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sansanee Auephanwiriyakul
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Keun Ho Ryu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Keun Ho Ryu .

Editor information

Editors and Affiliations

  1. Department of Applied Informatics, Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand

    Kietikul Jearanaitanakij

  3. Faculty of Computer Science and Information, University Teknologi Malaysia, Kuala Lumpur, Malaysia

    Ali Selamat

  4. Department of Applied Informatics, Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

  5. King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand

    Suphamit Chittayasothorn

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Munkhdalai, L., Li, M., Theera-Umpon, N., Auephanwiriyakul, S., Ryu, K.H. (2020). VAR-GRU: A Hybrid Model for Multivariate Financial Time Series Prediction. In: Nguyen, N., Jearanaitanakij, K., Selamat, A., Trawiński, B., Chittayasothorn, S. (eds) Intelligent Information and Database Systems. ACIIDS 2020. Lecture Notes in Computer Science(), vol 12034. Springer, Cham. https://doi.org/10.1007/978-3-030-42058-1_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-42058-1_27

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-42057-4

  • Online ISBN: 978-3-030-42058-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation