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Deterministic Method for the Prediction of Time Series

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Hard and Soft Computing for Artificial Intelligence, Multimedia and Security (ACS 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 534))

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Abstract

The business analyst is frequently forced to make decision using data on a certain business process obtained within a short time interval. Under these conditions, the analyst is not in a position to use traditional statistical methods and should be satisfied with experimental samples that are few in number. The paper deals with the new method for the prediction of time series based on the concepts of system identification. The suggested method shows a reasonable flexibility and accuracy when the analyzed process possesses a certain regularity property, and it is usable for the prediction of time series in a short-term perspective.

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Author information

Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, Zolnierska 49, 71-210, Szczecin, Poland

    Walery Rogoza

Authors
  1. Walery Rogoza
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Correspondence to Walery Rogoza .

Editor information

Editors and Affiliations

  1. Graduate School of Science and Engineeri, Ehime University Graduate School of Science and Engineeri, Ehime, Japan

    Shin-ya Kobayashi

  2. West Pomeranian University of Technology in Szczecin , Szczecin, Poland

    Andrzej Piegat

  3. West Pomeranian University of Technology in Szczecin , Szczecin, Poland

    Jerzy Pejaś

  4. West Pomeranian University of Technology in Szczecin , Szczecin, Poland

    Imed El Fray

  5. Polish Academy of Sciences, Systems Research Institute Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

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Rogoza, W. (2017). Deterministic Method for the Prediction of Time Series. In: Kobayashi, Sy., Piegat, A., Pejaś, J., El Fray, I., Kacprzyk, J. (eds) Hard and Soft Computing for Artificial Intelligence, Multimedia and Security. ACS 2016. Advances in Intelligent Systems and Computing, vol 534. Springer, Cham. https://doi.org/10.1007/978-3-319-48429-7_7

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  • DOI: https://doi.org/10.1007/978-3-319-48429-7_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48428-0

  • Online ISBN: 978-3-319-48429-7

  • eBook Packages: EngineeringEngineering (R0)

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