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Splitting Algorithm for Detecting Structural Changes in Predictive Relationships

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Advances in Data Mining. Applications and Theoretical Aspects (ICDM 2016)

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Abstract

Change point analysis is crucial in many different fields of science because real world data are full of instability. In this paper, we introduce a new parametric technique that allows to perform multiple structural change point analysis in a single-dependent variable relationship. The main idea in the splitting method is a heuristic smart search for structural breaks with identification of corresponding significant variables at each stable period.

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Acknowledgements

I would gratefully acknowledge the help, support and very constructive comments from Assistant Professors Pauliina Ilmonen and Pekka Malo from Aalto University, Helsinki. I am also thankful for the support from Yrj Uitto Foundation, the grant 13253583 of The Academy of Finland and the Foundation for Economic Education.

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Authors and Affiliations

  1. Department of Information and Service Economy, Aalto School of Business, Runeberginkatu 14-16, 00100, Helsinki, Finland

    Olga Gorskikh

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  1. Olga Gorskikh
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Correspondence to Olga Gorskikh .

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Editors and Affiliations

  1. IBaI, Inst of Comp Vision and applied Comp Sci, Leipzig, Sachsen, Germany

    Petra Perner

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Gorskikh, O. (2016). Splitting Algorithm for Detecting Structural Changes in Predictive Relationships. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2016. Lecture Notes in Computer Science(), vol 9728. Springer, Cham. https://doi.org/10.1007/978-3-319-41561-1_30

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  • DOI: https://doi.org/10.1007/978-3-319-41561-1_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41560-4

  • Online ISBN: 978-3-319-41561-1

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