Forecasting complex multi-component time series within systems designed to detect anomalies in dataflows of industrial automated systems-Reference-Cited by-同舟云学术

Forecasting complex multi-component time series within systems designed to detect anomalies in dataflows of industrial automated systems

Published:2019 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 12th International Conference on Security of Information and Networks - SIN '19
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Author:

Ragozin A. N.¹,Telezhkin V. F.¹,Podkorytov P. S.¹

Affiliation:

1. The South-Ural State University, Chelyabinsk, Russia

Publisher

ACM Press

Reference22 articles.

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2. A. Ragozin, V.Telezhkin, P.Podkorytov. Prediction of Aggregate Multicomponent Time Series in Industrial Automated Systems Using Neural Network, Lecture Notes in Engineering and Computer Science: Proceedings of The International MultiConference of Engineers and Computer Scientists 2019, 13--15 March, 2019, Hong Kong, pp17--19.

3. I. S. Kobersi and V. V. Shadrina, "Use of neural networks for energy management," Izvestiya SFedU. Engineering sciences, no. 7 (84), pp. 190--195.

4. V. N. Zuyeva and D. A. Trukhan, "Regression methods for predicting the load schedule of electrical equipment of enterprises, organizations and institutions: monograph," Kuban State Technological University. Krasnodar: KubSTU, 2017, pp. 123--135.

5. A. P. Shuravin and S. V. Vologdin, "Filtering input data of neural network using digital signal processing technologies," Bulletin of Kalashnikov ISTU, no. 4, pp. 104--109, 2017.

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