Modeling and analysis of fof2 data using narx neural networks and wavelets

The need to detect anomalies is of particular relevance in the problems of geophysical monitoring, it requires ensuring the accuracy and efficiency of the method. The paper proposes an approach based on NARX neural networks for the problem of modeling foF2 data and detecting anomalies in them. It is known that neural networks are difficult to model highly noisy and essentially non- stationary time series. Therefore, the optimization of the process of modeling time series of a complex structure by the NARX network was performed using wavelet filtering. Using the example of processing time series of ionospheric parameters, the effectiveness of the proposed approach is shown, and the results for the problem of detecting ionospheric anomalies are presented. The approach can be applied when performing a space weather forecast to predict the parameters of the ionosphere.