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Transfer Information Energy: A Quantitative Causality Indicator Between Time Series

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Artificial Neural Networks and Machine Learning – ICANN 2017 (ICANN 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10614))

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Abstract

We introduce an information-theoretical approach for analyzing cause-effect relationships between time series. Rather than using the Transfer Entropy (TE), we define and apply the Transfer Information Energy (TIE), which is based on Onicescu’s Information Energy. The TIE can substitute the TE for detecting cause-effect relationships between time series. The advantage of using the TIE is computational: we can obtain similar results, but faster. To illustrate, we compare the TIE and the TE in a machine learning application. We analyze time series of stock market indexes, with the goal to infer causal relationships between them (i.e., how they influence each other).

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Notes

  1. 1.

    Clive Granger, recipient of the 2003 Nobel Prize in Economics.

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Author information

Authors and Affiliations

  1. Electronics and Computers Department, Transilvania University, Braşov, Romania

    Angel Caţaron & Răzvan Andonie

  2. Computer Science Department, Central Washington University, Ellensburg, WA, USA

    Răzvan Andonie

Authors
  1. Angel Caţaron
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  2. Răzvan Andonie
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Corresponding author

Correspondence to Răzvan Andonie .

Editor information

Editors and Affiliations

  1. University of Lausanne, Lausanne, Switzerland

    Alessandra Lintas

  2. University of Genoa, Genoa, Italy

    Stefano Rovetta

  3. Universitat Pompeu Fabra, Barcelona, Spain

    Paul F.M.J. Verschure

  4. University of Lausanne, Lausanne, Switzerland

    Alessandro E.P. Villa

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Caţaron, A., Andonie, R. (2017). Transfer Information Energy: A Quantitative Causality Indicator Between Time Series. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10614. Springer, Cham. https://doi.org/10.1007/978-3-319-68612-7_58

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  • DOI: https://doi.org/10.1007/978-3-319-68612-7_58

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68611-0

  • Online ISBN: 978-3-319-68612-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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