Analyzing subtle features of natural time series by means of a wavelet-based approach - Mandrikova, O. V.; Solovjev, I. S.; Geppener, V. V.; Klionskiy, D. M. (Pattern recognition and image analysis, 2012)
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).