Segmented Least Squares

Michal Mankowski*, Mikhail Moshkov

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


The least squares method is widely used in statistics with common application to regression. The aim is to fit a linear function to set of points that minimizes the sum of the squares of the residuals. In some cases, fitting a linear function with a relatively small error is impossible. Keeping the linear character of approximation, the data points can be split into a sequence of segments, where to each of the segments the line given by a linear function is fitted. The optimization objectives for this problem is to minimize the total least squares error for all segments and to minimize the number of segments used. We refer to such a problem as segmented least squares.

Original languageEnglish (US)
Title of host publicationStudies in Systems, Decision and Control
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages10
StatePublished - 2021

Publication series

NameStudies in Systems, Decision and Control
ISSN (Print)2198-4182
ISSN (Electronic)2198-4190

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Control and Systems Engineering
  • Automotive Engineering
  • Social Sciences (miscellaneous)
  • Economics, Econometrics and Finance (miscellaneous)
  • Control and Optimization
  • Decision Sciences (miscellaneous)


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