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Synergy of econometric approach and use of neural networks to determine factors of provision of transport and logistics infrastructure in regions of Russia

The article justifies actuality of application of neural network methods for identification of significant predictors of the transport and logistics infrastructure of regions of the Russian Federation. The condition of the logistics industry of the Russian Federation in comparison with foreign countries has been analyzed. It was concluded that it is necessary to increase the accuracy of estimation of indicators of transport and logistics infrastructure of regions in order to identify their impact on the development of logistics. The problem of the traditional methodology of building a model of transport and logistics infrastructure of regions based on the application of mathematical and econometric analysis lies in the inability of the latter to find and accurately describe the non-obvious dependencies in the data. The expediency of sequential coupling of econometric and neural network research tools has been determined. The two-step procedure of identification of factors influencing the logistics development of the Russian Federation has been tested. As a result, it was possible to select the most significant socio-economic (average per capita income of the population, retail trade turnover, imports of the subjects of the Russian Federation) and infrastructure factors (the share of paved roads, the shipment of goods by public rail, the departure of passengers by public rail, the density of public railway) logistics infrastructure on the basis of an econometric approach. In the second step of the study, a neural network model of the remaining factors was developed based on the development of classification trees and a neural network, acting as a kind of computational filter, which allowed solving the problem of attribution of macroeconomic data and achieving a high level of significance of forecasts. The proposed approach of sequential coupling of econometric methods and neural network modelling has universality and practical importance, therefore it is applicable to the study of a wide range of macroeconomic processes. Read more...

Building and analyzing a machine learning model for short-term bitcoin market forecasting based on recurrent neural networks

In this article, the construction and analysis of machine learning models were performed for short-term forecasting in the cryptocurrency market on the example of bitcoin – one of the most popular cryptocurrencies in the world. The initial data for the study leads to the conclusion that over the long period of its existence, bitcoin has shown a high degree of volatility, especially evident in comparison with traditional financial instruments. The article substantiates that this market is influenced by a multitude of factors. No one can say for sure what makes up the value of a particular cryptocurrency, as it involves a range of reasons, which cannot be fully taken into account. To overcome this problem, we have considered the principle of recurrent neural network. It is described why networks with memory are better at making predictions on the time series than conventional autoregressive model and standard forward propagation networks. The initial data processing algorithm and transformation methods are defined. The sample was reduced in order to increase the speed of the network, by reducing the number of recalculations of weights. The algorithm of the family of recurrent neural networks was built and trained to test the hypothesis about their better adaptivity due to short-term and long-term memory. The model is evaluated on the test data representing the bitcoin exchange rate for 2021–2022, since this period is characterized by high volatility. It is concluded that it is reasonable to use a similar type of models for short-term forecasting of cryptocurrency rates. Read more...

Energy management of IIoT devices in electric power systems based on neuro-fuzzy models

The article presents the results of a study on optimizing the energy consumption of Industrial Internet of Things devices providing telemetry and included in the control loops of electric power systems. The relevance of the study lies in the emerging need to increase the battery life of mobile telemetry devices of industrial and technological systems, which helps to reduce the costs of their maintenance and support in working condition. An algorithm for controlling the parameters of the processor core of mobile devices with ARM architecture processors is proposed, which ensures higher energy efficiency of such devices used in the Industrial Internet of Things of the EPS. The novelty of the obtained results is the proposed algorithm for software control of the configuration of the ARM processor core parameters of mobile telemetry devices of the EPS, ensuring its higher energy efficiency, which is achieved by using data mining methods in its structure – a bidirectional neural network of long short-term memory and a fuzzy logical inference system. The choice of this network architecture is due to its ability to identify relationships in temporal sequences of device parameters by viewing the sequence in two directions at once – from the beginning to the end and vice versa. The network operates in the energy consumption plan classification mode, the results of which are then fed to the input of the fuzzy logic inference system to predict the optimal parameters of the ARM processor, which together forms a neuro-fuzzy model for managing the energy consumption of IIoT devices. Using machine learning libraries in the Python language in the Google Colab environment, model experiments were conducted, as a result of which the classification accuracy using a bidirectional neural network exceeded 0.8, and the standard deviation was 0.058 when predicting the parameters of the ARM processor based on the fuzzy logic inference system. Read more...