A self-normalization and support vector regression based approach for detecting structural change points in time series.

Xie N

DOI: 10.1371/journal.pone.0340729
Published: 2026
Container: PloS one
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1371/journal.pone.0340729,
  title = {A self-normalization and support vector regression based approach for detecting structural change points in time series.},
  author = {Xie N},
  year = {2026},
  journal = {PloS one},
  doi = {10.1371/journal.pone.0340729},
  url = {https://doi.org/10.1371/journal.pone.0340729}
}

RIS

TY  - JOUR
TI  - A self-normalization and support vector regression based approach for detecting structural change points in time series.
AU  - Xie N
PY  - 2026
JO  - PloS one
DO  - 10.1371/journal.pone.0340729
UR  - https://doi.org/10.1371/journal.pone.0340729
ER  -

APA

N, X. (2026). A self-normalization and support vector regression based approach for detecting structural change points in time series.. PloS one. https://doi.org/10.1371/journal.pone.0340729

Source records

pubmed · retrieved 2026-09-25T13:07:31.908Z