Development of forecasting theory and methods for developing forecasts in electroenergetics

The article discusses several fundamental problems in forecasting problems that form the scientific novelty of the research. One of the problems is formulated as the need to determine the key parameters that form the basis of the forecasting model and allow determining the state of the subject area....

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Главные авторы:	Tikhonov, E. E., Тихонов, Э. Е.
Formato:	Статья
Idioma:	English
Publicado em:	Institute of Electrical and Electronics Engineers Inc. 2021
Assuntos:	Catastrophe theory Forecasts Machine learning Neural networks Deep learning Chaos theory
Acesso em linha:	https://dspace.ncfu.ru/handle/20.500.12258/14713
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spelling	ir-20.500.12258-147132024-10-18T10:48:04Z Development of forecasting theory and methods for developing forecasts in electroenergetics Tikhonov, E. E. Тихонов, Э. Е. Catastrophe theory Forecasts Machine learning Neural networks Deep learning Chaos theory The article discusses several fundamental problems in forecasting problems that form the scientific novelty of the research. One of the problems is formulated as the need to determine the key parameters that form the basis of the forecasting model and allow determining the state of the subject area. The next key problem in the literature is formulated as the curse of dimensionality. Occurs when the researcher tries to take into account the maximum number of indicators and criteria for evaluating the subject area in the model, and this leads to the fact that the computer model required for its solution approaches the Turing limit. The third problem is described in the literature as the problem of supersystem. All elements of the predicted system or process can form higher-level systems that have their own unique properties. This makes it fundamentally impossible to describe a super-system mapping of target functions from the point of view of the systems and processes that make up the super-system. To develop the theory of forecasting and overcome these problems, we propose the use of such sections of modern mathematics as neuromathematics and deep machine learning, chaos theory, catastrophe theory, and the theory of self-organizing systems. It is believed that these methods will increase the depth of the forecast by identifying hidden patterns and relationships among poorly formalized conventional methods of predictive indicators 2021-01-18T13:04:49Z 2021-01-18T13:04:49Z 2020 Статья Tikhonov, E.E., Chebanov, K.A., Burlyaeva, V.A. Development of forecasting theory and methods for developing forecasts in electroenergetics // 2020 International Multi-Conference on Industrial Engineering and Modern Technologies, FarEastCon 2020. - 2020. - Номер статьи 9271504 http://hdl.handle.net/20.500.12258/14713 en 2020 International Multi-Conference on Industrial Engineering and Modern Technologies, FarEastCon 2020 application/pdf Institute of Electrical and Electronics Engineers Inc.
institution	СКФУ
collection	Репозиторий
language	English
topic	Catastrophe theory Forecasts Machine learning Neural networks Deep learning Chaos theory
spellingShingle	Catastrophe theory Forecasts Machine learning Neural networks Deep learning Chaos theory Tikhonov, E. E. Тихонов, Э. Е. Development of forecasting theory and methods for developing forecasts in electroenergetics
description	The article discusses several fundamental problems in forecasting problems that form the scientific novelty of the research. One of the problems is formulated as the need to determine the key parameters that form the basis of the forecasting model and allow determining the state of the subject area. The next key problem in the literature is formulated as the curse of dimensionality. Occurs when the researcher tries to take into account the maximum number of indicators and criteria for evaluating the subject area in the model, and this leads to the fact that the computer model required for its solution approaches the Turing limit. The third problem is described in the literature as the problem of supersystem. All elements of the predicted system or process can form higher-level systems that have their own unique properties. This makes it fundamentally impossible to describe a super-system mapping of target functions from the point of view of the systems and processes that make up the super-system. To develop the theory of forecasting and overcome these problems, we propose the use of such sections of modern mathematics as neuromathematics and deep machine learning, chaos theory, catastrophe theory, and the theory of self-organizing systems. It is believed that these methods will increase the depth of the forecast by identifying hidden patterns and relationships among poorly formalized conventional methods of predictive indicators
format	Статья
author	Tikhonov, E. E. Тихонов, Э. Е.
author_facet	Tikhonov, E. E. Тихонов, Э. Е.
author_sort	Tikhonov, E. E.
title	Development of forecasting theory and methods for developing forecasts in electroenergetics
title_short	Development of forecasting theory and methods for developing forecasts in electroenergetics
title_full	Development of forecasting theory and methods for developing forecasts in electroenergetics
title_fullStr	Development of forecasting theory and methods for developing forecasts in electroenergetics
title_full_unstemmed	Development of forecasting theory and methods for developing forecasts in electroenergetics
title_sort	development of forecasting theory and methods for developing forecasts in electroenergetics
publisher	Institute of Electrical and Electronics Engineers Inc.
publishDate	2021
url	https://dspace.ncfu.ru/handle/20.500.12258/14713
work_keys_str_mv	AT tikhonovee developmentofforecastingtheoryandmethodsfordevelopingforecastsinelectroenergetics AT tihonovée developmentofforecastingtheoryandmethodsfordevelopingforecastsinelectroenergetics
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Development of forecasting theory and methods for developing forecasts in electroenergetics

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