ArticleStatistical papers (Berlin, Germany)2020
Multiple change point detection and validation in autoregressive time series data.
Article in Statistical papers (Berlin, Germany), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Epidemic changepoint detection in the presence of nuisance changes.Statistical papers (Berlin, Germany) · 2023Article
- A greedy graph search algorithm based on changepoint analysis for automatic QRS complex detection.Computers in biology and medicine · 2021Article
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Authors and funding
3 authors.
Funding
Abstract
It is quite common that the structure of a time series changes abruptly. Identifying these change points and describing the model structure in the segments between these change points is of interest. In this paper, time series data is modelled assuming each segment is an autoregressive time series with possibly different autoregressive parameters. This is achieved using two main steps. The first step is to use a likelihood ratio scan based estimation technique to identify these potential change points to segment the time series. Once these potential change points are identified, modified parametric spectral discrimination tests are used to validate the proposed segments. A numerical study is conducted to demonstrate the performance of the proposed method across various scenarios and compared against other contemporary techniques.
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Registered trials
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