Modeling and forecasting production indices using artificial neural networks, taking into account intersectoral relationships and comparing the predictive qualities of various architectures

Моделирование и прогнозирование индексов производства при помощи искусственных нейронных сетей с учетом межотраслевых связей и сравнение прогностических качеств различных архитектур

Andrey Kaukin and Kosarev Vladimir (kosarev-vs@ranepa.ru)
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Kosarev Vladimir: Russian Presidential Academy of National Economy and Public Administration, https://www.ranepa.ru/eng/

Working Papers from Russian Presidential Academy of National Economy and Public Administration

Abstract: This paper analyzes the possibilities of using convolutional and recurrent neural networks to predict the indices of industrial production of the Russian economy. Since the indices are asymmetric in periods of growth and decline, it was hypothesized that nonlinear methods will improve the quality of the forecast relative to linear ones.

Keywords: convolutional neural networks; recurrent neural networks (search for similar items in EconPapers)
Pages: 78 pages
Date: 2021-01
New Economics Papers: this item is included in nep-big, nep-cis, nep-cmp and nep-for
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Persistent link: https://EconPapers.repec.org/RePEc:rnp:wpaper:s21105

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