Науковий вісник НГУ

Applied aspects of utilization of the group method of data handling for short-term forecasting

• 
• 

User Rating:  / 0

PoorBest 

Details

Category: Information technologies, systems analysis and administration

Last Updated on 08 February 2016

Published on 08 February 2016

Hits: 4436

Applied aspects of utilization of the group method of data handling for short-term forecasting

Authors:

O.V. Skakalina, Cand. Sci. (Tech.), Poltava National Technical Yuri Kondratyuk University, Senior Lecturer of the Department of Computers, Information Technologies and Systems, Poltava, Ukraine.

Abstract:

Purpose. The creation of the information apparatus for short-term forecasting of key indicators of economic entities (EE) industrial activity based on combinatorial and neural network algorithms of the group method of data handling (GMDH).

Methodology. This work presents a methodology for short-term forecasting of main economic indicators of industrial activity of an economic entity using modifications in the classical algorithm of the GMDH, comparing the effectiveness of combinatorial and neural network algorithms of the GMDH.

Findings. The analysis of forecasting methods reasoned the choice of the neural network algorithm of the GMDH for the research. The optimal model structure has been compiled and the dependence of the output parameters on the selected input parameters of the system has been derived. The analysis of economic indicators of an agricultural holding has been done. The research results are presented via graphic visualization. The effectiveness of the application of the modified GMDH algorithms for forecasting production trends for up to 3 years has been proved. The regularities of the data changes were revealed. The accuracy of the results was evaluated.

Originality. On the basis of the content analysis of the main indicators characterizing the agricultural holding industrial activity, new approaches to the construction of optimal models were proposed, taking into account characteristics of the subject area, which improves the quality of managerial decision making. Information technology for short-term forecasting based on neural network algorithm of the GMDH has been developed.

Practical value. The proposed technique allowed us to conduct the short-term forecasting of main economic indicators of an economic entity industrial activity (selling goods and services, cost of the sold products, gross profit, net profit; commercial, general and administrative expenses). The obtained accuracy assessment of the forecasting results is within the range from 1 to 5%.

References:

1. Ивахненко А.Г. Моделирование сложных систем по экспериментальным данным / Ивахненко А.Г., Юрачковский Ю.П. – М.: Радио и связь, 1987. – 120 с.

Ivakhnenko, A.G. and Yurachkovskiy, Yu.P. (1987), Modelirovaniye slozhnykh sistem po eksperimentalnym dannym [Modeling of Complex Systems Based on Experimental Data], Radio i Sviaz, Moscow 120 p.

2. Бояринов Ю.Г. Использование нейро-нечеткого метода группового учета аргументов для построения моделей региональных социально-экономических систем / Бояринов Ю.Г. // Информационные технологии моделирования и управления. − 2006. − Вып. 4(29). – С. 6−12.

Boyarinov, Yu.G. (2006), “Use of neuro-fuzzy group method of accounting arguments for building models of regional socio-economic systems”, Informatsyonnye Tekhnologii Modelirovaniya i Kontrolya, Vol. 4(29), pp. 6−12.

3. ЗайченкоЮ.П. Порівняльний аналіз прогнозуючих моделей, побудованих за допомогою нечітких алгоритмів МГУА з використанням різних алгоритмів генерації нечітких прогнозуючих моделей / Ю.П. Зайченко, І.О.Заєць // Матеріали міжнар. Семінару з індуктивного моделювання. – К.: НАН України, МННінформ. технол. Та систем, 2007. − №.4. − С. 158−165.

Zaichenko, Yu.P. and Zaiets, I.O. (2007), “Comparative analysis of the predictive models built using fuzzy algorithms and fuzzy GMDH using different algorithms for generating fuzzy prediction models”, Proc. of the Intern. Conf. on Inductive Modelling, Kyiv: National Academy of Sciences of Ukraine, no.4, pp. 158‒165.

4. Бидюк П.И. Анализ качества оценок прогнозов с использованием метода комплексирования / П.И. Бидюк, А.С. Гасанов, С.Е. Вавилов // Системні дослідження та інформаційні технології – 2013. − №4. − С. 7−16.

Biduk, P.I., Hasanov, A.S. and Vavilov, S.Ye. (2013), Analysis of the Quality Assessments of forecasts using the method of complexation, Systemni Doslidzhennia ta Informatsiini Technolohii, no.4, pp. 7−16.

5. Синеглазов В.М. Метод решения задачи прогнозирования на основе комплексирования оценок / В.М. Синеглазов, Е.И. Чумаченко, В.С. Горбатюк // Індуктивне моделювання складних систем. – 2012. − Вип. 4. − С. 214−223.

Sineglazov, V.M., Chumachenko, Ye.I. and Gorbatyuk, V.S. (2012), “Method of solving the problem of forecasting based on a variety of estimates”, Induktyvne Modeliuvannia Skladnykh System, Vol. 4, pp. 214−223.

6. Литвинов В.В. Создание блочных моделей систем и процессов с использованием метода группового учета аргументов / В.В. Литвинов, А.А. Задорожний // Математичні машини і системи. – 2012. − №2. − С.107 −116.

Litvinov, V.V. and Zadorozhny, A.A. (2012), “Creating block models of systems and processes using the group method of data handling”, Matematychni Mashyny i Systemy, no.2, pp. 107−116.

7. GMDH Shell, available at: http://www.machine learning.ru/GMDH_Shell

2015_06_skakalina
2016-02-08  984.79 KB  1045

• Next >