Prediction of unobserved bifurcation by unsupervised extraction of slowly time-varying system parameter dynamics from time series using reservoir computing
IntroductionNonlinear and non-stationary processes are prevalent in various natural and physical phenomena, where system dynamics can change qualitatively due to bifurcation phenomena. Machine learning methods have advanced our ability to learn and predict such systems from observed time series data...
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Frontiers Media S.A.
2024-10-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2024.1451926/full |
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| author | Keita Tokuda Yuichi Katori |
| author_facet | Keita Tokuda Yuichi Katori |
| author_sort | Keita Tokuda |
| collection | DOAJ |
| description | IntroductionNonlinear and non-stationary processes are prevalent in various natural and physical phenomena, where system dynamics can change qualitatively due to bifurcation phenomena. Machine learning methods have advanced our ability to learn and predict such systems from observed time series data. However, predicting the behavior of systems with temporal parameter variations without knowledge of true parameter values remains a significant challenge.MethodsThis study uses reservoir computing framework to address this problem by unsupervised extraction of slowly varying system parameters from time series data. We propose a model architecture consisting of a slow reservoir with long timescale internal dynamics and a fast reservoir with short timescale dynamics. The slow reservoir extracts the temporal variation of system parameters, which are then used to predict unknown bifurcations in the fast dynamics.ResultsThrough experiments on chaotic dynamical systems, our proposed model successfully extracted slowly varying system parameters and predicted bifurcations that were not included in the training data. The model demonstrated robust predictive performance, showing that the reservoir computing framework can handle nonlinear, non-stationary systems without prior knowledge of the system's true parameters.DiscussionOur approach shows potential for applications in fields such as neuroscience, material science, and weather prediction, where slow dynamics influencing qualitative changes are often unobservable. |
| format | Article |
| id | doaj-art-e6b5207fb76d481b8361b4c2d6ca2f7e |
| institution | OA Journals |
| issn | 2624-8212 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-e6b5207fb76d481b8361b4c2d6ca2f7e2025-08-20T02:08:42ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122024-10-01710.3389/frai.2024.14519261451926Prediction of unobserved bifurcation by unsupervised extraction of slowly time-varying system parameter dynamics from time series using reservoir computingKeita Tokuda0Yuichi Katori1Faculty of Health Data Science, Juntendo University, Urayasu, JapanThe School of Systems Information Science, Future University Hakodate, Hakodate, JapanIntroductionNonlinear and non-stationary processes are prevalent in various natural and physical phenomena, where system dynamics can change qualitatively due to bifurcation phenomena. Machine learning methods have advanced our ability to learn and predict such systems from observed time series data. However, predicting the behavior of systems with temporal parameter variations without knowledge of true parameter values remains a significant challenge.MethodsThis study uses reservoir computing framework to address this problem by unsupervised extraction of slowly varying system parameters from time series data. We propose a model architecture consisting of a slow reservoir with long timescale internal dynamics and a fast reservoir with short timescale dynamics. The slow reservoir extracts the temporal variation of system parameters, which are then used to predict unknown bifurcations in the fast dynamics.ResultsThrough experiments on chaotic dynamical systems, our proposed model successfully extracted slowly varying system parameters and predicted bifurcations that were not included in the training data. The model demonstrated robust predictive performance, showing that the reservoir computing framework can handle nonlinear, non-stationary systems without prior knowledge of the system's true parameters.DiscussionOur approach shows potential for applications in fields such as neuroscience, material science, and weather prediction, where slow dynamics influencing qualitative changes are often unobservable.https://www.frontiersin.org/articles/10.3389/frai.2024.1451926/fullchaosnonlinear dynamicsbifurcation (mathematics)reservoir computingslow - fast dynamics |
| spellingShingle | Keita Tokuda Yuichi Katori Prediction of unobserved bifurcation by unsupervised extraction of slowly time-varying system parameter dynamics from time series using reservoir computing Frontiers in Artificial Intelligence chaos nonlinear dynamics bifurcation (mathematics) reservoir computing slow - fast dynamics |
| title | Prediction of unobserved bifurcation by unsupervised extraction of slowly time-varying system parameter dynamics from time series using reservoir computing |
| title_full | Prediction of unobserved bifurcation by unsupervised extraction of slowly time-varying system parameter dynamics from time series using reservoir computing |
| title_fullStr | Prediction of unobserved bifurcation by unsupervised extraction of slowly time-varying system parameter dynamics from time series using reservoir computing |
| title_full_unstemmed | Prediction of unobserved bifurcation by unsupervised extraction of slowly time-varying system parameter dynamics from time series using reservoir computing |
| title_short | Prediction of unobserved bifurcation by unsupervised extraction of slowly time-varying system parameter dynamics from time series using reservoir computing |
| title_sort | prediction of unobserved bifurcation by unsupervised extraction of slowly time varying system parameter dynamics from time series using reservoir computing |
| topic | chaos nonlinear dynamics bifurcation (mathematics) reservoir computing slow - fast dynamics |
| url | https://www.frontiersin.org/articles/10.3389/frai.2024.1451926/full |
| work_keys_str_mv | AT keitatokuda predictionofunobservedbifurcationbyunsupervisedextractionofslowlytimevaryingsystemparameterdynamicsfromtimeseriesusingreservoircomputing AT yuichikatori predictionofunobservedbifurcationbyunsupervisedextractionofslowlytimevaryingsystemparameterdynamicsfromtimeseriesusingreservoircomputing |