Cargando…
A Novel Method for Fast Change-Point Detection on Simulated Time Series and Electrocardiogram Data
Although Kolmogorov-Smirnov (KS) statistic is a widely used method, some weaknesses exist in investigating abrupt Change Point (CP) problems, e.g. it is time-consuming and invalid sometimes. To detect abrupt change from time series fast, a novel method is proposed based on Haar Wavelet (HW) and KS s...
Autores principales: | Qi, Jin-Peng, Zhang, Qing, Zhu, Ying, Qi, Jie |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3972110/ https://www.ncbi.nlm.nih.gov/pubmed/24690633 http://dx.doi.org/10.1371/journal.pone.0093365 |
Ejemplares similares
-
Multiple change point detection and validation in autoregressive time series data
por: Ma, Lijing, et al.
Publicado: (2020) -
A Weighted Error Distance Metrics (WEDM) for Performance Evaluation on Multiple Change-Point (MCP) Detection in Synthetic Time Series
por: Qi, Jin Peng., et al.
Publicado: (2022) -
A Fast Framework for Abrupt Change Detection Based on Binary Search Trees and Kolmogorov Statistic
por: Qi, Jin-Peng, et al.
Publicado: (2016) -
Methods for Detecting Early Warnings of Critical Transitions in Time Series Illustrated Using Simulated Ecological Data
por: Dakos, Vasilis, et al.
Publicado: (2012) -
Arrhythmia classification detection based on multiple electrocardiograms databases
por: Qi, Meng, et al.
Publicado: (2023)