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Multivariate Chaotic Time Series Prediction: Broad Learning System Based on Sparse PCA

  • Weijie Li
  • Min HanEmail author
  • Shoubo Feng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11306)

Abstract

The sparse principle component analysis (SPCA) comprehensively considers the maximal variance of principal components and the sparseness of the load factor, thus making up for the defects of the traditional PCA. In this paper, we are committed to propose a novel approach based on broad learning system with sparse PCA named as SPCA-BLS for chaotic time series prediction. We also develop the incremental learning algorithms to rapidly rebuild the network without full retraining if the network is considered to be expanded. The core of the SPCA-BLS is that we achieved the dimensionality reduction and the features extraction of high-dimensional data and the dynamical reconstruction of the network without the entire retraining. The method has been simulated on an artificial and an actual data sets and the experimental results in regression accuracy confirm the characteristics and effectiveness of the SPCA-BLS.

Keywords

Chaotic time series prediction Broad learning system Sparse PCA 

Notes

Acknowledgment

This work is supported by the National Natural Science Foundation of China (Grant No. 61773087) and the Fundamental Research Funds for the Central Universities (DUT17ZD216).

References

  1. 1.
    Weigend, A.S.: Time series prediction: forecasting the future and understanding the past. Routledge (2018)Google Scholar
  2. 2.
    Han, M., Zhong, K., Qiu, T., Han, B.: Interval type-2 fuzzy neural networks for chaotic time series prediction: a concise overview. IEEE Trans. Cybern. (2018).  https://doi.org/10.1109/TCYB.2018.2834356
  3. 3.
    Ak, R., Fink, O., Zio, E.: Two machine learning approaches for short-term wind speed time-series prediction. IEEE Trans. Neural Netw. Learn. Syst. 27(8), 1734–1747 (2016)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Han, M., Ren, W., Xu, M., Qiu, T.: Nonuniform state space reconstruction for multivariate chaotic time series. IEEE Trans. Cybern. (2018).  https://doi.org/10.1109/TCYB.2018.2816657
  5. 5.
    Jolliffe, I.T., Cadima, J.: Principal component analysis: a review and recent developments. Phil. Trans. R. Soc. A 374(2065) (2016). 20150202MathSciNetCrossRefGoogle Scholar
  6. 6.
    Hu, Z., Pan, G., Wang, Y., Wu, Z.: Sparse principal component analysis via rotation and truncation. IEEE Trans. Neural Netw. Learn. Syst. 27(4), 875–890 (2016)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Zou, H., Xue, L.: A selective overview of sparse principal component analysis. Proc. IEEE 106(8), 1311–1320 (2018)CrossRefGoogle Scholar
  8. 8.
    Jolliffe, I.T., Trendafilov, N.T., Uddin, M.: A modified principal component technique based on the lasso. J. Comput. Graph. Stat. 12(3), 531–547 (2003)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Zou, H., Hastie, T., Tibshirani, R.: Sparse principal component analysis. J. Comput. Graph. Stat. 15(2), 265–286 (2006)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Chen, C.P., Liu, Z.: Broad learning system: an effective and efficient incremental learning system without the need for deep architecture. IEEE Trans. Neural Netw. Learn. Syst. 29(1), 10–24 (2018)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Takens, F.: Detecting strange attractors in turbulence. In: Rand, D., Young, L.-S. (eds.) Dynamical Systems and Turbulence, Warwick 1980. LNM, vol. 898, pp. 366–381. Springer, Heidelberg (1981).  https://doi.org/10.1007/BFb0091924CrossRefGoogle Scholar
  12. 12.
    Huang, Y., Kou, G., Peng, Y.: Nonlinear manifold learning for early warnings in financial markets. Eur. J. Oper. Res. 258(2), 692–702 (2017)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. J. R. Stat. Soc.: Ser. B (Stat. Methodol.) 67(2), 301–320 (2005)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Jaeger, H., Lukoševičius, M., Popovici, D., Siewert, U.: Optimization and applications of echo state networks with leaky-integrator neurons. Neural Netw. 20(3), 335–352 (2007)CrossRefGoogle Scholar
  15. 15.
    Lorenz, E.N.: Deterministic nonperiodic flow. J. Atmos. Sci. 20(2), 130–141 (1963)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Faculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalianChina

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