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The geomagnetic field varies in time and space, being a signal composed of periodic and non-periodic elements and is monitored in a tri-axial system within the geomagnetic observatories of the INTERMAGNET network across the planet. In the work, based on these data, we use for spectral evaluation (Fourier, wavelet, signal decompositions in approximations and details with various functions on different levels, etc.) in order to analyze different geomagnetic phenomena (such as: geomagnetic storms, disturbances, etc.)...

Through the complex analysis of the geomagnetic database of the Surlari Geomagnetic Observatory for over 80 years, corroborated with the data of other planetary observatories from the INTERMAGNET network, we have extracted the geomagnetic information that is correlated with the specific physical parameters of space weather domain. Space weather refers to the conditions and phenomena occurring in outer space that can influence the near-Earth space environment. This concept includes a variety of events and condition...

The main objectives of this study are: analysis of the associated spectrum of the geomagnetic field, time of occurrence of geomagnetic storms and comparisons between recordings made at various geomagnetic observatories in the INTERMAGNET network, in terms of frequency intensity identified and correlations during geomagnetic disturbances. A geomagnetic storm is a temporary disturbance of the Earth's magnetosphere caused by ejections of solar corona mass, coronal holes or solar flares. The data used in this paper ar...

Geomagnetic disturbances and their associated phenomena can be an important parameter as data input in climatology studies and climate change. This paper is a preamble to a deliverable "Development of a catalog on the typology of geomagnetic variations based on records from the Surlari Geomagnetic Observatory" within the Space Weather project which is part of the complex multidisciplinary project „Institutional capacities and services for research, monitoring and forecasting of risks in extra-atmospheric space” wh...

The Auto-Regressive Integrated Moving Average (ARIMA) model is widely used to forecast non-stationary time series data. In a model of ARIMA (p, d, q), AR is autoregressive, p is the number of regression terms, MA is the moving average, q is the number of moving average terms, and d is the difference time to make the data a stationary series. It can be used to forecast the trend of geomagnetic disturbances. Firstly, the non-stationary historical data xt is processed by the d difference to develop the stable histori...