Сравнительный анализ методов прогнозирования временных рядов на основе нейронных сетей и регрессионного анализа

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Опубликован: сен 16, 2021
Ключевые слова:
гибридные модели, временной ряд, нейронные сети, нечеткие методы моделирования, регрессионный анализ, эконометрические методы hybrid models, time series, neural networks, fuzzy modeling methods, regression analysis, econometric methods

Основное содержимое статьи

А.Н. Аверкин
ГБОУ ВО «Международный Университет природы, общества и человека «Дубна», Институт системного анализа и управления
С.А. Ярушев
ГБОУ ВО «Международный Университет природы, общества и человека «Дубна», Институт системного анализа и управления

Аннотация

В статье рассматриваются два основных направления в прогнозировании временных рядов, а именно, нейросетевые методы прогнозирования и методы на основе регрессионного анализа. Производится сравнение результатов прогноза на примере отдельных показателей, произведенных на основе двух методов. Анализируются основные проблемы, возникающие при использовании данных методов, а так же методы их решения, в частности гибридизация данных методов. Проводится широкий обзор исследований по сравнению прогностической производительности методов на основе искусственных нейронных сетей и других методов прогнозирования. Особое внимание уделяется сравнению методов ИНС и методов множественной регрессии. 

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Аверкин, А. и Ярушев, С. 2021. Сравнительный анализ методов прогнозирования временных рядов на основе нейронных сетей и регрессионного анализа . Системный анализ в науке и образовании. 2 (сен. 2021), 34–49.
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№ 2 (2015): №2 (2015)
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