Systems and Means of Informatics
2018, Volume 28, Issue 3, pp 62-71
FORECASTING MOMENTS OF FINITE NORMAL MIXTURES USING FEEDFORWARD NEURAL NETWORKS
- A. K. Gorshenin
- V. Yu. Kuzmin
Abstract
Modeling and analysis of nonstationary data flows in real systems of various types can be effectively performed using finite local-scale normal mixtures. Approbation of the prediction methodology developed by the authors is carried out on the example of time-varied moments of the mixed probability model. Within this approach, values of the initial continuous time-series are replaced with the discrete ones and then modified samples are analyzed with a neural network. For short-term forecasting, the accuracy of more than 80% is demonstrated. Feedforward neural network is implemented using the Keras deep learning library, the TensorFlow framework, and the Python programming language.
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[+] About this article
Title
FORECASTING MOMENTS OF FINITE NORMAL MIXTURES USING FEEDFORWARD NEURAL NETWORKS
Journal
Systems and Means of Informatics
Volume 28, Issue 3, pp 62-71
Cover Date
2018-09-30
DOI
10.14357/08696527180305
Print ISSN
0869-6527
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
finite normal mixtures; moments; artificial neural network; forecasting; deep learning; data mining
Authors
A. K. Gorshenin  ,  and V. Yu. Kuzmin 
Author Affiliations
Institute of Informatics Problems, Federal Research Center "Computer Science and Control", Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
Faculty of Computational Mathematics and Cybernetics, M.V. Lomonosov Moscow State University, 1-52 Leninskiye Gory, GSP-1, Moscow 119991, Russian Federation
"Wi2Geo LLC", 3-1 Mira Ave., Moscow 129090, Russian Federation
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