Optimal Segmented Linear Regression for Financial Time Series Segmentation

Chi Jen Wu, Wei Sheng Zeng, Jan Ming Ho

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

3 Scopus citations

Abstract

Given a financial time series dataset, one of the most fundamental and interesting challenges is the need to learn the stock dynamics signals in the financial time series data. An essential task is to represent the time series in line segments which is often used as a pre-processing step for learning the marketing signal patterns in financial computing. In this paper, we focus on the optimization problem of computing the best segmentations of such time series based on segmented linear regression models. The major contribution of this paper is to define the problem of Multi-Segment Linear Regression (MSLR) of computing the optimal segmentation of a financial time series, denoted as the MSLR problem, such that the global square error of segmented linear regression is minimized. We present an optimum algorithm named OMSLR, with two-level dynamic programming (DP) design, and show the optimality of OMSLR algorithm. The two-level DP design of OMSLR algorithm can mitigate the complexity of searching the best trading strategies in financial markets. It runs in O(kn2) time, where n is the length of the time series sequence and k is the number of non-overlapping segments that cover all data points.

Original languageEnglish
Title of host publicationProceedings - 21st IEEE International Conference on Data Mining Workshops, ICDMW 2021
EditorsBing Xue, Mykola Pechenizkiy, Yun Sing Koh
PublisherIEEE Computer Society
Pages623-630
Number of pages8
ISBN (Electronic)9781665424271
DOIs
StatePublished - 2021
Event21st IEEE International Conference on Data Mining Workshops, ICDMW 2021 - Virtual, Online, New Zealand
Duration: 07 12 202110 12 2021

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
Volume2021-December
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259

Conference

Conference21st IEEE International Conference on Data Mining Workshops, ICDMW 2021
Country/TerritoryNew Zealand
CityVirtual, Online
Period07/12/2110/12/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

  • piecewise polynomial representation
  • segmentation
  • segmented linear regression
  • signal learning and processing
  • time-series

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