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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Pattern recognition and image analysis Jg. 22; H. 2; S. 323 - 332
Hauptverfasser: Mandrikova, O. V., Solovjev, I. S., Geppener, V. V., Klionskiy, D. M.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Moscow Nauka/Interperiodica 01.04.2012
Springer Nature B.V
Schlagworte:
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
multiscale structure
wavelets
Earth’s magnetic field
geophysical data
perturbations
natural time series
best wavelet basis
ISSN:1054-6618, 1555-6212
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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).
Bibliographie:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-2
content type line 23
ISSN:1054-6618
1555-6212
DOI:10.1134/S1054661812020083