Splitting algorithm for detecting structural changes in predictive relationships

Olga Gorskikh*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


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.

Original languageEnglish
Title of host publicationAdvances in Data Mining: Applications and Theoretical Aspects - 16th Industrial Conference, ICDM 2016, Proceedings
PublisherSpringer Verlag
Number of pages15
ISBN (Electronic)978-3-319-41561-1
ISBN (Print)978-3-319-41560-4
Publication statusPublished - 2016
MoE publication typeA4 Article in a conference publication
EventIndustrial Conference on Advances in Data Mining - New York, United States
Duration: 13 Jul 201617 Jul 2016
Conference number: 16

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)03029743
ISSN (Electronic)16113349


ConferenceIndustrial Conference on Advances in Data Mining
Abbreviated titleICDM
CountryUnited States
CityNew York


  • Change point analysis
  • Parametric technique
  • Predictive relationship

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