Skip to main content
SpringerLink
Log in

Predicting Multivariate Time Series Using Subspace Echo State Network

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

Echo state network (ESN) is a novel kind of recurrent neural networks, where a reservoir is generated randomly and only readout layer is adaptable. It outperforms conventional recurrent neural networks in the field of multivariate time series prediction. Often ESN works beautifully. But sometimes it works poorly because of ill-posed problem. To solve it, we propose a new model on the basis of ESNs, termed as fast subspace decomposition echo state network (FSDESN). The core of the model is to utilize fast subspace decomposition algorithm for extracting a compact subspace out of a redundant large-scale reservoir matrix in order to remove approximate collinear components, overcome the ill-posed problem, and improve generalization performance. Experimental results on two multivariate benchmark datasets substantiate the effectiveness and characteristics of FSDESN.

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

Similar content being viewed by others

References

  1. Cordova J, Yu W (2012) Two types of Haar wavelet neural networks for nonlinear system identification. Neural Process Lett 35:283–300

    Article  Google Scholar 

  2. Sapankevych N, Sankar R (2009) Time series prediction using support vector machines: a survey. IEEE Comput Intell Mag. doi:10.1109/MCI.2009.932254

  3. Chen SM, Chang YC (2011) Weighted fuzzy rule interpolation based on GA-based weight-learning techniques. IEEE Trans Fuzzy Syst 19(4):729–744

    Article  Google Scholar 

  4. Gil PG, Cortes JMR, Hernández SEP, Aquino VA (2011) A neural network scheme for long-term forecasting of chaotic time series. Neural Process Lett 33:215–233

    Article  Google Scholar 

  5. Li TS, Feng G, Wang D, Tong SC (2010) Neural-network-based simple adaptive control of uncertain multi-input multi-output non-linear systems. IET Control Theory Appl 4(5):1543–1557

    Article  MathSciNet  Google Scholar 

  6. Li TS, Wang D, Feng G, Tong SC (2010) A DSC approach to robust adaptive NN tracking control for strict-feedback nonlinear systems. IEEE Trans Syst Man Cybern B Cybern 40(3):915–927

    Article  Google Scholar 

  7. Wen SP, Zeng ZG, Huang TW (2012) Exponential stability analysis of memristor-based recurrent neural networks with time-varying delays. Neurocomputing 97:233–240

    Article  Google Scholar 

  8. Jaeger H, Haas H (2004) Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 304:78–80

    Article  Google Scholar 

  9. LukošEvičIus M, Jaeger H (2009) Reservoir computing approaches to recurrent neural network training. Comput Sci Rev 3:127–149

    Article  MATH  Google Scholar 

  10. Rodan A, Tiňo P (2011) Minimum complexity echo state network. IEEE Trans Neural Netw 22(1):131–144

    Article  Google Scholar 

  11. Shi ZW, Han M (2007) Support vector echo-state machine for chaotic time-series prediction. IEEE Trans Neural Netw 18(2):359–372

    Article  Google Scholar 

  12. Chatzis SP, Demiris Y (2011) Echo state Gaussian process. IEEE Trans Neural Netw 22(9):1435–1445

    Article  Google Scholar 

  13. Leung CS, Lam PM (2003) A local training–pruning approach for recurrent neural networks. Int J Neural Syst 13:25–38

    Article  Google Scholar 

  14. Friedman JH (2012) Fast sparse regression and classification. Int J Forecast 28:722–738

    Article  Google Scholar 

  15. Dutoit X, Schrauwen B, Campenhout JV, Stroobandt D, Brussel HV, Nuttin M (2009) Pruning and regularization in reservoir computing. Neurocomputing 72:1534–1546

    Article  Google Scholar 

  16. Reichel L, Sgallari F, Ye Q (2012) Tikhonov regularization based on generalized Krylov subspace methods. Appl Numer Math 62(9):1215–1228

    Article  MATH  MathSciNet  Google Scholar 

  17. Tang X, Han M (2009) Partial Lanczos extreme learning machine for single-output regression problems. Neurocomputing 72:3066–3076

    Article  Google Scholar 

  18. Mahadevan S (2008) Fast spectral learning using Lanczos eigenspace projections. In: Proceedings of the twenty-third AAAI conference on artifical intelligence, Chicago, pp 1472–1474

  19. Xu G, Kailath T (1994) Fast subspace decomposition. IEEE Trans Signal Process 42(3):539–551

    Article  MathSciNet  Google Scholar 

  20. Rubio F, Mestre X (2009) Generalized consistent estimation on low-rank Krylov subspaces of arbitrarily high dimension. IEEE Trans Signal Process 57(10):3787–3800

    Article  MathSciNet  Google Scholar 

  21. Han M, Wang Y (2009) Prediction of multivariate time series based on reservoir principal component analysis. Control Decis 24(10):1526–1530

    MATH  Google Scholar 

Download references

Acknowledgments

This research is supported by the project (61074096) of the National Natural Science Foundation of China.

Author information

Authors and Affiliations

  1. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Liaoning, 116023, China

    Min Han & Meiling Xu

Authors
  1. Min Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Meiling Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Min Han.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Han, M., Xu, M. Predicting Multivariate Time Series Using Subspace Echo State Network. Neural Process Lett 41, 201–209 (2015). https://doi.org/10.1007/s11063-013-9324-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-013-9324-7

Keywords

Search

Navigation