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An Optimal Algorithm for Combining Multivariate Forecasts in Hybrid Systems

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2774))

Abstract

The problem of combining of multivariate forecasts produced by different components in a hybrid system is considered. An algorithm for combining of the forecasts on a finite sample is proposed and its optimality is proven. Recurrent procedure for real-time processing based on Gauss-Newton method is developed. The presented approach provides improvement of the solution’s precision in forecasting problems.

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References

  1. Bishop, C.M.: Neural Networks for Pattern Recognition, p. 482. Clarendon Press, Oxford (1995)

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  2. The Great Energy Predictor Shootout – The first building data analysis and prediction competition, ftp://ftp.cs.colorado.edu/pub/cs/energy-shootout/

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Author information

Authors and Affiliations

  1. Control Systems Research Laboratory, Kharkiv National University of Radioelectronics, 14 Lenin av., Kharkiv, 61166, Ukraine

    Ye. Bodyanskiy, I. Pliss & S. Popov

  2. Institute of Automatic Control and System Engineering, Technical University of Ilmenau, PF 100565, Ilmenau, 98684, Germany

    P. Otto

Authors
  1. Ye. Bodyanskiy
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  2. P. Otto
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  3. I. Pliss
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  4. S. Popov
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Editor information

Editors and Affiliations

  1. Computing Laboratory, Oxford University, Parks Road, OXI 3QD, Oxford, United Kingdom

    Vasile Palade

  2. Centre for SMART Systems, School of Environment and Technology, University of Brighton, BN2 4GJ, Brighton, UK

    Robert J. Howlett

  3. Knowledge-Based Intelligent Engineering Systems Centre, University of South Australia, Mawson Lakes, SA 5095, Adelaide, Australia

    Lakhmi Jain

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© 2003 Springer-Verlag Berlin Heidelberg

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Bodyanskiy, Y., Otto, P., Pliss, I., Popov, S. (2003). An Optimal Algorithm for Combining Multivariate Forecasts in Hybrid Systems. In: Palade, V., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2003. Lecture Notes in Computer Science(), vol 2774. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45226-3_132

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  • DOI: https://doi.org/10.1007/978-3-540-45226-3_132

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40804-8

  • Online ISBN: 978-3-540-45226-3

  • eBook Packages: Springer Book Archive

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