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Segmenting Time Series via Self-Normalisation

Research article (Journal of the Royal Statistical Society Series B (Statistical Methodology), 2022) · cited 13× · AI/ML
academia openalex:W4307952118
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Segmenting Time Series via Self-Normalisation

Summary

Segmenting Time Series via Self-Normalisation is a scholarly article[1].

Key Facts

  • Segmenting Time Series via Self-Normalisation's instance of is recorded as scholarly article[2].

References

Programmatic citations — every numbered marker resolves to a verifiable graph row below.

Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

Class ancestry

  1. [1] ↑ . Wikidata. wikidata.org.

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Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.

APA 4ort.xyz Knowledge Graph. (2026). Segmenting Time Series via Self-Normalisation. Retrieved May 24, 2026, from https://4ort.xyz/entity/segmenting-time-series-via-self-normalisation
MLA “Segmenting Time Series via Self-Normalisation.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/segmenting-time-series-via-self-normalisation.
BibTeX @misc{4ortxyz_segmenting-time-series-via-self-normalisation_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Segmenting Time Series via Self-Normalisation}}, year = {2026}, url = {https://4ort.xyz/entity/segmenting-time-series-via-self-normalisation}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Segmenting Time Series via Self-Normalisation — https://4ort.xyz/entity/segmenting-time-series-via-self-normalisation (retrieved 2026-05-24)

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