Latent Stochastic Differential Equations for Change Point Detection
Automated analysis of complex systems based on multiple readouts remains a challenge. Change point detection algorithms are aimed to locating abrupt changes in the time series behaviour of a process. In this paper, we present a novel change point detection algorithm based on Latent Neural Stochastic...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10261192/ |
| _version_ | 1797668027689336832 |
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| author | Artem Ryzhikov Mikhail Hushchyn Denis Derkach |
| author_facet | Artem Ryzhikov Mikhail Hushchyn Denis Derkach |
| author_sort | Artem Ryzhikov |
| collection | DOAJ |
| description | Automated analysis of complex systems based on multiple readouts remains a challenge. Change point detection algorithms are aimed to locating abrupt changes in the time series behaviour of a process. In this paper, we present a novel change point detection algorithm based on Latent Neural Stochastic Differential Equations (SDE). Our method learns a non-linear deep learning transformation of the process into a latent space and estimates a SDE that describes its evolution over time. The algorithm uses the likelihood ratio of the learned stochastic processes in different timestamps to find change points of the process. We demonstrate the detection capabilities and performance of our algorithm on synthetic and real-world datasets. The proposed method outperforms the state-of-the-art algorithms on the majority of our experiments. |
| first_indexed | 2024-03-11T20:23:06Z |
| format | Article |
| id | doaj.art-31ab0b2b0ed8441baed7cca7883d8ae6 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T20:23:06Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-31ab0b2b0ed8441baed7cca7883d8ae62023-10-02T23:00:19ZengIEEEIEEE Access2169-35362023-01-011110470010471110.1109/ACCESS.2023.331831810261192Latent Stochastic Differential Equations for Change Point DetectionArtem Ryzhikov0https://orcid.org/0000-0002-3543-0313Mikhail Hushchyn1Denis Derkach2Computer Science Department, National Research University Higher School of Economics (HSE University), Moscow, RussiaComputer Science Department, National Research University Higher School of Economics (HSE University), Moscow, RussiaComputer Science Department, National Research University Higher School of Economics (HSE University), Moscow, RussiaAutomated analysis of complex systems based on multiple readouts remains a challenge. Change point detection algorithms are aimed to locating abrupt changes in the time series behaviour of a process. In this paper, we present a novel change point detection algorithm based on Latent Neural Stochastic Differential Equations (SDE). Our method learns a non-linear deep learning transformation of the process into a latent space and estimates a SDE that describes its evolution over time. The algorithm uses the likelihood ratio of the learned stochastic processes in different timestamps to find change points of the process. We demonstrate the detection capabilities and performance of our algorithm on synthetic and real-world datasets. The proposed method outperforms the state-of-the-art algorithms on the majority of our experiments.https://ieeexplore.ieee.org/document/10261192/Anomaly detectionchange point detectiondeep learningmachine learningtimeseries |
| spellingShingle | Artem Ryzhikov Mikhail Hushchyn Denis Derkach Latent Stochastic Differential Equations for Change Point Detection IEEE Access Anomaly detection change point detection deep learning machine learning timeseries |
| title | Latent Stochastic Differential Equations for Change Point Detection |
| title_full | Latent Stochastic Differential Equations for Change Point Detection |
| title_fullStr | Latent Stochastic Differential Equations for Change Point Detection |
| title_full_unstemmed | Latent Stochastic Differential Equations for Change Point Detection |
| title_short | Latent Stochastic Differential Equations for Change Point Detection |
| title_sort | latent stochastic differential equations for change point detection |
| topic | Anomaly detection change point detection deep learning machine learning timeseries |
| url | https://ieeexplore.ieee.org/document/10261192/ |
| work_keys_str_mv | AT artemryzhikov latentstochasticdifferentialequationsforchangepointdetection AT mikhailhushchyn latentstochasticdifferentialequationsforchangepointdetection AT denisderkach latentstochasticdifferentialequationsforchangepointdetection |