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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]

The results of modeling and analysis of ionospheric parameters during disturbed geomagnetic field are presented. Hourly and 15-min data of the ionospheric critical frequency (foF2) at Paratunka site (Kamchatka, Russia, 53.0 N and 158.7 E) for the period 1968–2019 were applied. The modeling was based on ionospheric parameter multicomponent model developed by the authors. In order to refine model parameters, three classes of anomalies were introduced. They characterize strong, moderate and weak ionospheric disturbances. On the basis of the modeling, we detected short-period ionospheric disturbances of moderate and weak intensity, preceding magnetic storms. They characterize occurrences of oscillatory processes in the ionosphere at the background of increased solar activity. Comparisons with the International Reference Ionosphere model (IRI-2016) and the median method were made. That showed the efficiency of the proposed model. For automatic performance of the approach, computational solutions were suggested to detect and to estimate ionospheric disturbance parameters. • The method of ionospheric data analysis allows to study ionospheric disturbances. • An abnormal increase in the ionosphere on the eve of magnetic storms was identified. • The comparison of the proposed method with IRI showed its high efficiency. [ABSTRACT FROM AUTHOR]

Abstract In this work, according to the ground observation data, we study the dynamics of the ionospheric parameters during periods of strong and moderate magnetic storm of 2015–2017. The study was carried out using new methods of modeling and data analysis proposed by the authors. Multicomponent model of ionospheric parameters presented in this work allows describing changes of the ionospheric parameters, both in the conditions of the calm ionosphere and in perturbed periods. During the study, we allocated ionospheric disturbances preceding and accompanying the periods of magnetic storms in analyzing regions. The analysis showed a high incidence of the effect of an increase in the electron concentration of the ionosphere preceding the onset of magnetic storms. Comparing the results of the study with cosmic ray data, we also noticed the correlation of effects in the ionosphere with the anomalous changes in the dynamics of cosmic rays. Highlights • The method of ionospheric data analysis allows to study ionospheric disturbances. • An abnormal increase in the ionosphere on the eve of magnetic storms was identified. • There is a correlation between anomalies in cosmic rays and ionospheric disturbances. [ABSTRACT FROM AUTHOR]