Multiple change-point detection for some point processes
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866910686142005248 |
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| author | Dion-Blanc, C. Hawat, D. Lebarbier, E. Robin, S. |
| author_facet | Dion-Blanc, C. Hawat, D. Lebarbier, E. Robin, S. |
| contents | The aim of change-point detection is to identify behavioral shifts within time series data. This article focuses on scenarios where the data is derived from an inhomogeneous Poisson process or a marked Poisson process. We present a methodology for detecting multiple offline change-points using a minimum contrast estimator. Specifically, we address how to manage the continuous nature of the process given the available discrete observations. Additionally, we select the appropriate number of changes via a cross-validation procedure which is particularly effective given the characteristics of the Poisson process. Lastly, we show how to use this methodology to self-exciting processes with changes in the intensity. Through experiments, with both simulated and real datasets, we showcase the advantages of the proposed method, which has been implemented in the R package \texttt{CptPointProcess}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_09103 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Multiple change-point detection for some point processes Dion-Blanc, C. Hawat, D. Lebarbier, E. Robin, S. Methodology The aim of change-point detection is to identify behavioral shifts within time series data. This article focuses on scenarios where the data is derived from an inhomogeneous Poisson process or a marked Poisson process. We present a methodology for detecting multiple offline change-points using a minimum contrast estimator. Specifically, we address how to manage the continuous nature of the process given the available discrete observations. Additionally, we select the appropriate number of changes via a cross-validation procedure which is particularly effective given the characteristics of the Poisson process. Lastly, we show how to use this methodology to self-exciting processes with changes in the intensity. Through experiments, with both simulated and real datasets, we showcase the advantages of the proposed method, which has been implemented in the R package \texttt{CptPointProcess}. |
| title | Multiple change-point detection for some point processes |
| topic | Methodology |
| url | https://arxiv.org/abs/2302.09103 |