Analyzing subtle features of natural time series by means of a wavelet-based approach
The present paper is devoted to the development of methods and approaches intended for the analysis of natural time series. Due to the strong variability, irregularity, and complex structure of the time series in question, the problem of automatic processing, i.e., in automatic mode, is rather compl...
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| Veröffentlicht in: | Pattern recognition and image analysis Jg. 22; H. 2; S. 323 - 332 |
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| Sprache: | Englisch |
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Moscow
Nauka/Interperiodica
01.04.2012
Springer Nature B.V |
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| ISSN: | 1054-6618, 1555-6212 |
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| Abstract | The present paper is devoted to the development of methods and approaches intended for the analysis of natural time series. Due to the strong variability, irregularity, and complex structure of the time series in question, the problem of automatic processing, i.e., in automatic mode, is rather complicated and merits further investigation in order to produce better solutions than those that presently exist. Relying on contemporary methods of signal processing, signal analysis, and recognition of complex data, we have suggested a new wavelet-based approach, which allows one to extract subtle structural features from a complex natural time series in an automatic mode. After that, it becomes possible to identify these features and analyze them in terms of a particular knowledge domain. Our methods and approaches have been successfully tested on the Earth’s magnetic field data obtained from the Paratunka observatory (Paratunka village, Kamchatka region, Far East of Russia). |
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| AbstractList | The present paper is devoted to the development of methods and approaches intended for the analysis of natural time series. Due to the strong variability, irregularity, and complex structure of the time series in question, the problem of automatic processing, i.e., in automatic mode, is rather complicated and merits further investigation in order to produce better solutions than those that presently exist. Relying on contemporary methods of signal processing, signal analysis, and recognition of complex data, we have suggested a new wavelet-based approach, which allows one to extract subtle structural features from a complex natural time series in an automatic mode. After that, it becomes possible to identify these features and analyze them in terms of a particular knowledge domain. Our methods and approaches have been successfully tested on the Earth’s magnetic field data obtained from the Paratunka observatory (Paratunka village, Kamchatka region, Far East of Russia). The present paper is devoted to the development of methods and approaches intended for the analysis of natural time series. Due to the strong variability, irregularity, and complex structure of the time series in question, the problem of automatic processing, i.e., in automatic mode, is rather complicated and merits further investigation in order to produce better solutions than those that presently exist. Relying on contemporary methods of signal processing, signal analysis, and recognition of complex data, we have suggested a new wavelet-based approach, which allows one to extract subtle structural features from a complex natural time series in an automatic mode. After that, it becomes possible to identify these features and analyze them in terms of a particular knowledge domain. Our methods and approaches have been successfully tested on the Earth's magnetic field data obtained from the Paratunka observatory (Paratunka village, Kamchatka region, Far East of Russia).[PUBLICATION ABSTRACT] |
| Author | Mandrikova, O. V. Geppener, V. V. Klionskiy, D. M. Solovjev, I. S. |
| Author_xml | – sequence: 1 givenname: O. V. surname: Mandrikova fullname: Mandrikova, O. V. email: oksanam1@mail.kamchatka.ru organization: Institute of Cosmophysical Research and Radio Wave Propagation Far Eastern Branch of the Russian Academy of Sciences – sequence: 2 givenname: I. S. surname: Solovjev fullname: Solovjev, I. S. organization: Kamchatka State Technical University – sequence: 3 givenname: V. V. surname: Geppener fullname: Geppener, V. V. organization: St. Petersburg Electrotechnical University “LETI” – sequence: 4 givenname: D. M. surname: Klionskiy fullname: Klionskiy, D. M. organization: St. Petersburg Electrotechnical University “LETI” |
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| Cites_doi | 10.5194/angeo-19-395-2001 10.1029/91RG00994 10.1109/18.119732 10.5194/angeo-27-1521-2009 10.1029/TE044i004p00411 10.1109/TGE.1977.294510 10.1111/j.1365-246X.1995.tb06895.x 10.1007/BF00217388 10.5565/PUBLMAT_35191_06 10.1016/S1364-6826(96)00105-8 10.1190/1.1440649 10.1214/aos/1024691081 10.1046/j.1365-246x.1998.00438.x 10.1029/2000RS002340 10.1006/acha.1995.1008 10.1109/TAC.1980.1102471 10.1007/978-3-642-75894-2 10.1137/1.9781611970104 10.1016/B978-012466606-1/50008-8 |
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| DOI | 10.1134/S1054661812020083 |
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| Keywords | multiscale structure wavelets Earth’s magnetic field geophysical data perturbations natural time series best wavelet basis |
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| Snippet | The present paper is devoted to the development of methods and approaches intended for the analysis of natural time series. Due to the strong variability,... |
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| SubjectTerms | Analysis Computer Science Far East Fourier transforms Geophysics Image Processing and Computer Vision Image processing systems Magnetic fields Magnetism Natural time Neural networks Noise Pattern Recognition Processing Recognition Representation Russia Signal processing Studies Time series Understanding of Images Villages Wavelet transforms |
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| Title | Analyzing subtle features of natural time series by means of a wavelet-based approach |
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