A method for analyzing complex structured data with elements of machine learning
Bogdana Mandrikova
Abstract
A method for analyzing data of complex structure based on combining a wavelet transform and neural networks Autoencoder is proposed. The method allows you to research the data structure, detect abnormal changes of various shapes and durations, and suppress noise. The efficiency of the method is shown on the example of data from a network of neutron monitor stations. Neutron monitor data determine the intensity of secondary cosmic rays and are one of the key factors in space weather. The numerical implementation of the method allows it to be applied on-line, which is of interest in problems of analyzing environmental data and detecting catastrophic events.
Topics & Concepts
Computer scienceWaveletAutoencoderNoise (video)Artificial neural networkData miningArtificial intelligenceKey (lock)Space weatherPattern recognition (psychology)Wavelet transformSpace (punctuation)PhysicsMeteorologyImage (mathematics)Operating systemComputer securityEarthquake Detection and AnalysisStatistical and numerical algorithmsAdvanced Data Processing Techniques