Symbolic Dynamical Filtering via Variable Length Markov Model and Machine Learning
Time series are the natural way for accessing information about dynamical systems or processes in a variety of scientific, engineering and financial applications. Due to their complexity, the use of data-driven methods is imperative. An example of this method is the symbolic dynamic filtering (SDF)...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10418998/ |
| _version_ | 1797319553023213568 |
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| author | Higor Dos Santos Daniel P. B. Chaves Cecilio Pimentel |
| author_facet | Higor Dos Santos Daniel P. B. Chaves Cecilio Pimentel |
| author_sort | Higor Dos Santos |
| collection | DOAJ |
| description | Time series are the natural way for accessing information about dynamical systems or processes in a variety of scientific, engineering and financial applications. Due to their complexity, the use of data-driven methods is imperative. An example of this method is the symbolic dynamic filtering (SDF) technique, which involves the determination of Markovian models to express the causal structure of the observed dynamic behavior. This technique simplifies the data complexity by encapsulating the fundamental dynamics of the system into a symbolic sequence. The traditional application of SDF in time series analysis typically entails constructing a probabilistic finite state automaton (PFSA) based on an observed symbolic sequence. We propose a new algorithm for obtaining PFSAs models based on variable length Markov chains, machine learning algorithms and graph minimization techniques. To validate the algorithm, we provide modeling examples from simulated and experimental dataset, showing that the obtained model is superior to those generated by alternative techniques. Finally, we apply the proposed framework for anomalous detection of rotating machines. |
| first_indexed | 2024-03-08T04:08:39Z |
| format | Article |
| id | doaj.art-703b2aecd0fe4c1980a8eaa8cb1539a0 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T04:08:39Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-703b2aecd0fe4c1980a8eaa8cb1539a02024-02-09T00:03:31ZengIEEEIEEE Access2169-35362024-01-0112197781978910.1109/ACCESS.2024.336183910418998Symbolic Dynamical Filtering via Variable Length Markov Model and Machine LearningHigor Dos Santos0https://orcid.org/0009-0008-5719-1684Daniel P. B. Chaves1https://orcid.org/0000-0003-4345-4320Cecilio Pimentel2https://orcid.org/0000-0003-2632-919XDepartment of Electronics and Systems, Federal University of Pernambuco, Recife, BrazilDepartment of Electronics and Systems, Federal University of Pernambuco, Recife, BrazilDepartment of Electronics and Systems, Federal University of Pernambuco, Recife, BrazilTime series are the natural way for accessing information about dynamical systems or processes in a variety of scientific, engineering and financial applications. Due to their complexity, the use of data-driven methods is imperative. An example of this method is the symbolic dynamic filtering (SDF) technique, which involves the determination of Markovian models to express the causal structure of the observed dynamic behavior. This technique simplifies the data complexity by encapsulating the fundamental dynamics of the system into a symbolic sequence. The traditional application of SDF in time series analysis typically entails constructing a probabilistic finite state automaton (PFSA) based on an observed symbolic sequence. We propose a new algorithm for obtaining PFSAs models based on variable length Markov chains, machine learning algorithms and graph minimization techniques. To validate the algorithm, we provide modeling examples from simulated and experimental dataset, showing that the obtained model is superior to those generated by alternative techniques. Finally, we apply the proposed framework for anomalous detection of rotating machines.https://ieeexplore.ieee.org/document/10418998/Anomaly detectionclusteringgraph minimizationprobabilistic finite state automatontime series analysisvariable length Markov models |
| spellingShingle | Higor Dos Santos Daniel P. B. Chaves Cecilio Pimentel Symbolic Dynamical Filtering via Variable Length Markov Model and Machine Learning IEEE Access Anomaly detection clustering graph minimization probabilistic finite state automaton time series analysis variable length Markov models |
| title | Symbolic Dynamical Filtering via Variable Length Markov Model and Machine Learning |
| title_full | Symbolic Dynamical Filtering via Variable Length Markov Model and Machine Learning |
| title_fullStr | Symbolic Dynamical Filtering via Variable Length Markov Model and Machine Learning |
| title_full_unstemmed | Symbolic Dynamical Filtering via Variable Length Markov Model and Machine Learning |
| title_short | Symbolic Dynamical Filtering via Variable Length Markov Model and Machine Learning |
| title_sort | symbolic dynamical filtering via variable length markov model and machine learning |
| topic | Anomaly detection clustering graph minimization probabilistic finite state automaton time series analysis variable length Markov models |
| url | https://ieeexplore.ieee.org/document/10418998/ |
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