Detection of features in the series of electric field strength measurements using data transformation and machine learning

Abstract

Data transformation is a common information processing procedure that can detect features hidden in the data that are not visible in their original form. The article shows some examples of such processing to detect features in the data of long-term observations of the intensity of the surface atmospheric electric field. This uses the data of the EFM 550 field sensor installed in the city of Nalchik. The results are visualized using the Matplotlib Python library to visualize the results of such data processing. The selected data transformation methods are also well suited for machine learning. To demonstrate the possibilities of finding features, several methods of teaching without a teacher are used. It is noted that most of the identified features in the series of measurements of the surface atmospheric electric field are associated with local meteorological phenomena.