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Geomagnetic data analysis is an important basis for the investigation of the processes in the near-Earth space, Earth magnetosphere, and ionosphere. The negative impact of geomagnetic anomalies on modern technical objects and human health determine the applied significance of the investigation and requires the creation of effective methods for timely detection of the anomalies. Priory complicated structure of geomagnetic data makes their formalization and analysis difficult. This paper proposes a wavelet model for geomagnetic field variations. It describes characteristic changes and anomalies of different amplitude and duration. Numerical realization of the model provides the possibility to apply it in online analysis. We describe the process of model identification and show its efficiency in the detection of sudden, short-period geomagnetic anomalies occurring before and during magnetic storms. Raw second data of the Paratunka and Magadan observatories and post-processed minute data were used in the paper. The question of noise effect on the proposed model results was under consideration. [ABSTRACT FROM AUTHOR]

A hybrid model for the time series of complex structure (HMTS) was proposed. It is based on the combination of function expansions in a wavelet series with ARIMA models. HMTS has regular and anomalous components. The time series components, obtained after expansion, have a simpler structure that makes it possible to identify the ARIMA model if the components are stationary. This allows us to obtain a more accurate ARIMA model for a time series of complicated structure and to extend the area for application. To identify the HMTS anomalous component, threshold functions are applied. This paper describes a technique to identify HMTS and proposes operations to detect anomalies. With the example of an ionospheric parameter time series, we show the HMTS efficiency, describe the results and their application in detecting ionospheric anomalies. The HMTS was compared with the nonlinear autoregression neural network NARX, which confirmed HMTS efficiency. [ABSTRACT FROM AUTHOR]