The authors propose a general formulation of the economic and mathematical model of the problem of optimizing the size of the newly created peasant farms and Industrial Development, taking into account the chosen specialization of activity, as well as determining the optimal parameters for the already-known size of farms. The developed mathematical model differs from the classical one by the presence of additional blocks, which prescribe the sales channels of manufactured products and determine the necessary financial resources. The proposed methodological approach should be used for planning the development of regional economies, taking into account the existing specifics.
The article discusses an approach to the analysis of static information based on the use of neural network technologies and regression models. The forecast for the neural network was made on the basis of official statistics, data on the development of advanced technologies, and also taking into account the current situation in the dynamics of the incidence in Russia for the period from 2003 to 2020. As a result, forecasts for the next 5 years were determined. Growth is expected to continue, but at a slower pace. At this stage, the incidence and the introduction of quarantine measures adversely affect the situation and lead to a reduction in consumption. It is worth noting that the trends in the production of innovative technologies will continue, despite the negative consequences of the pandemic. This is evidenced by forecast data.
This article focuses on supervised learning and reinforcement learning. These areas overlap most with econometrics, predictive modelling, and optimal control in finance. We choose to focus on how to cast machine learning into various financial modelling and decision frameworks. This work introduces the industry context for machine learning in finance, discussing the critical events that have shaped the finance industry’s need for machine learning and the unique barriers to adoption. The finance industry has adopted machine learning to varying degrees of sophistication. Some key examples demonstrate the nature of machine learning and how it is used in practice. In particular, we begin to address many finance practitioner’s concerns that neural networks are a “black-box” by showing how they are related to existing well-established techniques such as linear regression, logistic regression, and autoregressive time series models. Neural networks can be shown to reduce to other well-known statistical techniques and are adaptable to time series data.
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