Ordinal pattern-based change point detection

The ordinal patterns of a fixed number of consecutive values in a time series are the spatial ordering of these values. Counting how often a specific ordinal pattern occurs in a time series provides important insights into the properties of the time series. In this work, we prove the asymptotic normality of the relative frequency of ordinal patterns for time series with linear increments. Moreover, we apply ordinal patterns to detect changes in the distribution of a time series.

Authors

Institutions

Publication Details

Journal
Test
Published
2025-08-15
DOI
https://doi.org/10.1007/s11749-025-00983-9
Citations
2
Primary Topic
Time Series Analysis and Forecasting
Type
article
Field-Weighted Citation Impact
2.97

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Ordinal pattern-based change point detection

Johannes Schmidt-Hieber, Annika Betken, Giorgio Micali
2 citations
Test
Time Series Analysis and Forecasting
2.97
article

Ordinal pattern-based change point detection

Johannes Schmidt-Hieber, Annika Betken, Giorgio Micali
article en
2 citations

Abstract

The ordinal patterns of a fixed number of consecutive values in a time series are the spatial ordering of these values. Counting how often a specific ordinal pattern occurs in a time series provides important insights into the properties of the time series. In this work, we prove the asymptotic normality of the relative frequency of ordinal patterns for time series with linear increments. Moreover, we apply ordinal patterns to detect changes in the distribution of a time series.

TestVol. 34(4)
University of Twente (NL)
Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Openalex Percentile: Top 8%
Time Series Analysis and Forecasting
2.97
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.