The article shows a study to assess the structure of the timber industry complex of Russia in the pioneering days of a new industrialization. It is a radically new stage of scientific and technological progress that has no historical analogues in the degree of influence on human civilization. New industrial technologies, possessing enormous potential for changing the direction of technological development, at the same time have ambiguous social and environmental consequences. The timber industry complex is one of the most important and interesting objects for research, since the products of the industry are distributed throughout the world and play an important role for the economies of all countries. In developed countries, the forest industry complex accounts for about 10% of the total industrial production. Wood is an important type of raw material. Even considering the competition from new technologies and materials, the proportion in the structure of the global gross domestic product is almost not reduced. The fundamental problem of assessing and predicting the effects of new industrialization based on technological transformation using quantitative indicators forms the need to create a multisigmoidal innovation cycle for changing the structure: from obtaining new fundamental knowledge to their practical use.

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The article discusses using of the recurrent neural networks technology to the multidimensional time series prediction problem. There is an experimental determination of the neural network architecture and its main hyperparameters carried out to achieve the minimum error. The revealed network structure going to be used further to detect anomalies in multidimensional time series.

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