Splitting algorithm for detecting structural changes in predictive relationships

Olga Gorskikh

doi:10.1007/978-3-319-41561-1_30

Splitting algorithm for detecting structural changes in predictive relationships

Olga Gorskikh^*

^*Corresponding author for this work

Department of Information and Service Management

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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.

Original language	English
Title of host publication	Advances in Data Mining: Applications and Theoretical Aspects - 16th Industrial Conference, ICDM 2016, Proceedings
Publisher	Springer Verlag
Pages	405-419
Number of pages	15
Volume	9728
ISBN (Electronic)	978-3-319-41561-1
ISBN (Print)	978-3-319-41560-4
DOIs	https://doi.org/10.1007/978-3-319-41561-1_30
Publication status	Published - 2016
MoE publication type	A4 Article in a conference publication
Event	Industrial Conference on Advances in Data Mining - New York, United States Duration: 13 Jul 2016 → 17 Jul 2016 Conference number: 16

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9728
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Conference

Conference	Industrial Conference on Advances in Data Mining
Abbreviated title	ICDM
Country	United States
City	New York
Period	13/07/2016 → 17/07/2016

Keywords

Change point analysis
Parametric technique
Predictive relationship

Access to Document

10.1007/978-3-319-41561-1_30

Link to publication in Scopus

Fingerprint Dive into the research topics of 'Splitting algorithm for detecting structural changes in predictive relationships'. Together they form a unique fingerprint.

Cite this

Gorskikh, O. (2016). Splitting algorithm for detecting structural changes in predictive relationships. In Advances in Data Mining: Applications and Theoretical Aspects - 16th Industrial Conference, ICDM 2016, Proceedings (Vol. 9728, pp. 405-419). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9728). Springer Verlag. https://doi.org/10.1007/978-3-319-41561-1_30

Gorskikh, Olga. / Splitting algorithm for detecting structural changes in predictive relationships. Advances in Data Mining: Applications and Theoretical Aspects - 16th Industrial Conference, ICDM 2016, Proceedings. Vol. 9728 Springer Verlag, 2016. pp. 405-419 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{f8e9a8ec4333425a887c1859dc364553,

title = "Splitting algorithm for detecting structural changes in predictive relationships",

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.",

keywords = "Change point analysis, Parametric technique, Predictive relationship",

author = "Olga Gorskikh",

year = "2016",

doi = "10.1007/978-3-319-41561-1_30",

language = "English",

isbn = "978-3-319-41560-4",

volume = "9728",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "405--419",

booktitle = "Advances in Data Mining: Applications and Theoretical Aspects - 16th Industrial Conference, ICDM 2016, Proceedings",

address = "Germany",

note = "Industrial Conference on Advances in Data Mining, ICDM ; Conference date: 13-07-2016 Through 17-07-2016",

}

Gorskikh, O 2016, Splitting algorithm for detecting structural changes in predictive relationships. in Advances in Data Mining: Applications and Theoretical Aspects - 16th Industrial Conference, ICDM 2016, Proceedings. vol. 9728, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9728, Springer Verlag, pp. 405-419, Industrial Conference on Advances in Data Mining, New York, United States, 13/07/2016. https://doi.org/10.1007/978-3-319-41561-1_30

Splitting algorithm for detecting structural changes in predictive relationships. / Gorskikh, Olga.

Advances in Data Mining: Applications and Theoretical Aspects - 16th Industrial Conference, ICDM 2016, Proceedings. Vol. 9728 Springer Verlag, 2016. p. 405-419 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9728).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Splitting algorithm for detecting structural changes in predictive relationships

AU - Gorskikh, Olga

N1 - Conference code: 16

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

KW - Change point analysis

KW - Parametric technique

KW - Predictive relationship

UR - http://www.scopus.com/inward/record.url?scp=84979084457&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-41561-1_30

DO - 10.1007/978-3-319-41561-1_30

M3 - Conference contribution

AN - SCOPUS:84979084457

SN - 978-3-319-41560-4

VL - 9728

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 405

EP - 419

BT - Advances in Data Mining: Applications and Theoretical Aspects - 16th Industrial Conference, ICDM 2016, Proceedings

PB - Springer Verlag

T2 - Industrial Conference on Advances in Data Mining

Y2 - 13 July 2016 through 17 July 2016

ER -

Gorskikh O. Splitting algorithm for detecting structural changes in predictive relationships. In Advances in Data Mining: Applications and Theoretical Aspects - 16th Industrial Conference, ICDM 2016, Proceedings. Vol. 9728. Springer Verlag. 2016. p. 405-419. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-41561-1_30