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Issues of Application of Machine Learning Methods in Forecasting the Formation of Prospective Sectors of the New Generation Economy
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Issues of Application of Machine Learning Methods in Forecasting the Formation of Prospective Sectors of the New Generation Economy
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S207751800032929-0-1
DOI
10.18254/S207751800032929-0
Publication type
Article
Статус публикации
Published
Authors
Alovsat Aliyev 
ORCID: 0000-0002-1174-8036
Occupation: Head of Department
Affiliation: Institute of Information Technology of the Ministry of Science and Education
Address: Azerbaijan, Baku
Roza Shahverdiyeva
ORCID: 0000-0003-0842-7300
Occupation: Senior Researcher
Affiliation: Institute of Information Technology of the Ministry of Science and Education, Azerbaijan Technical University
Address: Azerbaijan, Baku
Edition
Volume 19 Special Issue
Abstract

The article is devoted to the application of machine learning methods in predicting the formation of prospective sectors of the new generation economy. In the conditions of modern digital transformations, it has been shown that the replacement of the traditional existing economy with new generation economic models is one of the world's priority development directions. The relevance of the application of Machine learning (ML) methods, one of the artificial intelligence (AI) technologies, in improving the processes of formation and development of the traditional sectors of the economy, and in forecasting its new generation perspective sectors is substantiated. An analysis of scientific research works related to the problem was carried out. Digital transformation and technologies, sustainability and sustainability, greening technologies and circularity, joint use, smart decision-making and management, platforms and ecosystems, innovative entrepreneurship, research and economic development, inclusion, and social development, Industry 5.0 platform technologies of the formation of the new generation technological economy. the main basic principles such as transition etc. have been worked out and the problems of its formation have been analyzed. The features and perspectives of the application of machine learning methods in forecasting the prospective sectors of the new generation economy have been explained. Classification features of machine learning methods are explained and its models are shown. The structural scheme of the stages of economic development forecasting has been developed and information about its methods has been provided. A comparative analysis of machine learning methods applied in forecasting was carried out. The structural scheme of the application stages of the machine learning method in the forecasting process has been developed. Relevant recommendations on the application of Industry 4.0 platform technologies were given for forecasting the formation of prospective sectors of the new generation economy based on real data.

Keywords
digital transformation, artificial intelligence technologies, digital economy, technological innovation, digital innovative technologies, perspective sectors of the new generation economy, Industry 4.0 platform, Machine learning methods, forecasting
Received
28.09.2024
Date of publication
12.11.2024
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1

Introduction

The rapid development of digital transformation and artificial intelligence technologies is one of the main issues that ensure the world economy enters a new stage of development. Technological development has led to a significant change in global economic relations and the emergence of a new generation of technological economic sectors. The next-generation economic sectors are characterized by increased integration of communication, automation, artificial intelligence, and other digital technologies and innovations. In the current period, the application of Machine learning methods, one of the artificial intelligence technologies, is very relevant in improving the processes of formation and development of the traditional sectors of the economy, and in forecasting its new generation perspective sectors. Machine learning methods and artificial intelligence technologies are considered to be the main methods that allow more accurate and effective forecasting of future economic areas [5, 8].
2

The role of machine learning in the accurate analysis of economic processes and forecasting the development of prospective economic sectors is indispensable. In addition to traditional economic activities, the new generation economy envisages the development of a renewed economy consisting of digital finance, biotechnology, green technology, green energy, and sustainable technologies. In order to conduct an effective economic policy and make the right economic decisions in these areas, it is required to make highly accurate and long-term forecasts [8, 24].
3

It is no coincidence that in the Socio-Economic Development Strategy of Azerbaijan technological innovation factors include 1) effective integration of the country's economy into the global value chain, 2) through technological innovations, as well as state strengthening of resource and service provision in this field with private partnership, 3) formation of the necessary ecosystem for rapid adoption of technological innovations in the national economy, 4) transformation of the country into a green energy space, and 5) acceleration of environmental health processes, etc.
4 In the context of economic forecasting, Machine Learning methods can analyze a wide range of data, from market behavior to technological advances, and uncover hidden patterns that are not visible through traditional analytical approaches. For this reason, the presented article examines the application of machine learning methods in predicting the formation of prospective sectors of the new generation economy. These methods were considered as new scientific-technical supporting instruments and mechanisms for decision-makers, and some recommendations were given on their scientific-technical and practical application.
5

Statement of the problem

In the conditions of modern digital transformations, the replacement of the traditional existing economy with new generation economic models, as well as the digitalization of all areas of society and economy, the formation and rapid development of new perspective economic sectors continue to become one of the world's priority development criteria. The emerging generation of the national digital economy is currently developing rapidly in various sectors. In such conditions, the formation and development of prospective sectors of the New Generation economy should be based on relevant scientific and theoretical foundations. Forecasting of economic sectors should be based on advanced artificial intelligence methods, including machine learning methods, innovative digital and mathematical methods, and technologies. The essence of this problem, whose relevance is beyond doubt, is also the study of the characteristics of the formation of the new generation technological economy, the determination of development directions, as well as the development of a conceptual methodological approach and relevant recommendations for assessing the suitability of those areas.
6

Through the application of machine learning in the development of the economy, the analysis of information about new sectors of the economy is accelerated and areas with development potential are identified more quickly. At the same time, it becomes possible to predict future market demands, determine the development of various industries, and create more stable decision-making systems [17].
7 Machine learning models have the ability to provide more accurate and reliable results as a result of the analysis of the main factors affecting the formation of economic development. However, the application of machine learning methods is accompanied by some problems, including model interpretation, data quality, ethical issues, etc. The large scale and diversity of data in the application of machine learning methods in economic forecasting, and the fact that data in the fields of energy, healthcare, and digital finance are often diverse and unstructured can reduce the accuracy of models. There are also difficulties in interpreting machine learning models. Although they perform well, the results of those models are often difficult to interpret. This feature is of particular importance in the planning of new generation sectors of public administration and the economy.
8 Machine learning methods have great potential in predicting future economic development with their ability to process large amounts of data and uncover hidden relationships. Using data from a variety of sources, such as market trends, industry performance, technological advances, and consumer behavior, machine learning models can provide new insights and knowledge about which economic sectors will emerge or grow in importance. This predictive ability is particularly important in the next-generation economy, where traditional economic forecasting models struggle to keep up with the dynamic changes brought about by innovation. In general, the role of machine learning methods in the formation of promising sectors of the new generation economy is great. These methods offer an effective approach to both data analysis and forecasting of future market demands.
9

The formation of new generation technological economy sectors and technological development is reflected in many State Programs and official documents, where technological development is one of the main tasks for advanced countries, including Azerbaijan. In those programs, taking into account the great importance of technological development in the field of industrial development and the civil and military-industrial complex, its main goals were defined. It has been shown that technological development, digitalization, cyber security, and the application of artificial intelligence have become signs of daily life in the country and have been set as a task before the institutions and society.
10

Analysis of scientific research related to the problem

It is known that the application of cognitive technologies in solving many problems of digital innovative technologies in the economy, forecasting some of its processes and effective management has created new opportunities and conditions. Certain scientific studies have been conducted on this issue. It should be noted that the authors of the articles, like other researchers, have publications in this field in different years and in scientific journals indexed in different high scientific databases [1-5, 7, 8, 24]. The requirements for solving the relevant problems in those publications must be taken into account in the comprehensive formation of the new generation technological economy and in the analysis of development problems. For this reason, despite the fact that the analysis of the above-mentioned problems has been studied to a certain extent in the scientific works of many scientists and specialists, the demand for a deeper and comprehensive study of that process, as well as the development of various approaches to solving the problems that have arisen, still remains and remains relevant.
11

In [21], the issues of macrostructural analysis and forecasting of economic development in modern conditions were considered. Opportunities to assess the medium and long-term potential of economic growth using macroeconomic and intersectoral tools were presented. [23] is dedicated to a new paradigm for stimulating and forecasting China's economic growth in the medium and long term. Here, the system philosophy of certain relations is considered theoretically. Here, the supporting system of China's economic development is established for the three dimensions of economic growth, social development, and environmental protection. Based on this, based on the principles of system theory and system dynamics, and as a result of combining the theories of other related disciplines, an economic geography-system dynamics integrated forecasting model was built to simulate and quantitatively forecast China's medium and long-term economic growth.
12

In [16], the development of the Deep LSTM method for predicting the price of Bitcoin, one of the cryptocurrencies of the new generation economy, was considered. Here, a deep neural network architecture is proposed to predict the exchange rate of Bitcoin. The architecture is based on LSTM (Long-Short Term Memory), a type of recurrent neural network. The obtained results allow us to confirm that deep learning approaches can be quite productive in applying them to other issues of intellectual analysis of non-stationary time series related to cryptocurrencies.
13

A bibliometric analysis of published literature on machine learning in economics and econometrics was conducted in [12]. The purpose of the study is the analysis of data collected from publications indexed by the Web of Science and Scopus databases covering the years 1991-2020. Analysis of variance was used to determine the relationship between the number of article citations and years. In [15], Internet financial forecasting and digital economy development issues were analyzed using machine learning algorithms in the new consumer environment.
14

In [8], the conceptual bases of the application of artificial intelligence technologies in the forecasting of economic processes were developed. Artificial intelligence methods based on evolutionary algorithms, Data-mining technology, machine learning technology, and computer vision were analyzed in forecasting economic processes. The basics of applying the neoclassical production function in predicting the impact of artificial intelligence on economic growth have been investigated. Neoclassical models were used to investigate the impact of artificial intelligence technology on production. Based on data mining technology, the economic development potential forecasting model and its forecasting adequacy were explained. The structural scheme of the main components of the application of artificial intelligence technology in the forecasting of economic processes has been developed.
15

In [14], issues of cooperation between the state and business in forecasting and planning structural changes in the economy were considered. In this work, the Japanese experience of indicative planning during the reconstruction of the economic structure is presented. Based on a long-term view of the Japanese economy, the degree of involvement in the business planning process and its evaluation have been noted. In [22], the issue of predicting the income of the management company based on machine learning technologies was analyzed. Here, an algorithm based on the machine learning method is proposed for forecasting the earnings of a management company.
16

[13] on inflation rate forecasting and machine learning method analysis. Here, the analysis covered the years 2012-2022. It has been shown that the Random Forest model can predict the inflation rate more accurately than other machine learning models. Also, non-linear machine learning models have been proven to be more successful than linear machine learning or time series models.
17

New directions for applied knowledge-based artificial intelligence and machine learning are reviewed in [11]. Selected new directions in knowledge-based artificial intelligence and machine learning are presented here: ontology development methodologies and tools, automated engineering of WordNets, innovations in semantic search, automated machine learning, and AutoML. Knowledge-based AI and machine learning complement each other ideally, as their strengths compensate for each other's weaknesses. These are demonstrated through selected enterprise use cases (anomaly detection, efficient modeling of supply networks, circular economy, and semantic enrichment of technical information).
18

In [18] inflation forecasting in developing countries, machine learning for advanced forecasting and the integration of foreign exchange reserves are considered. Research results have shown that machine learning models consistently outperform traditional models, with Random Forest and Gradient Boosting being the best across different sets of determinants. In addition, the study finds that the inclusion of foreign exchange reserves as a determinant in the models has a positive effect on the predictive performance of both traditional and machine learning-based inflation forecasting models. As can be seen from the analysis, many aspects of the application of the machine learning method should be taken into account in the complex forecasting of the formation of prospective sectors of the new generation economy.
19

Applications of new generation digital technologies and transformation in the economy

The rapid rise of digital technologies has reshaped the global economy, driving the transformation of industries, business models, and consumer behaviour. Next-generation digital technologies such as artificial intelligence, blockchain, Internet of Things (IoT), Big Data analytics, and 5G networks are not only changing the way businesses operate but are also paving the way for a fully digitized economy. While digital transformation offers great opportunities for efficiency, innovation, and economic growth, it also brings challenges.
20

Cognitive technologies, which constitute the intellectual foundations of new generation digital technologies [6] are essentially software and equipment that imitate the activity of the human brain and work with the user. They try to assess a person's attention, monitor his condition, help his brain function, and generally "understand" him. These technologies also include artificial intelligence and machine learning. 1)Artificial intelligence and machine learning are revolutionizing the activities of various economic sectors. These technologies allow analyzing large volumes of data, improving decision-making, and automating complex tasks in the economy and business. Blockchain technology provides a decentralized, secure, and transparent way to record transactions. Its applications cover all industries, contributing to the development of new business models and digital ecosystems. Blockchain technology is used in trade financing, P2P transactions, and smart contracts, by reliably protecting the history of all recorded transactions and processes without falsification, based on a distributed (decentralized) database. During the emission ("production", mining) of cryptocurrency as a virtual currency, it is based on the special application of cryptographic algorithms. Internet of Things (IoT). technology also refers to an interconnected network of physical devices, sensors, and software that collect and exchange data. IoT has numerous applications in sectors such as agriculture, manufacturing, healthcare, and smart cities. The Internet of Things (IoT) is a set of technologies that involves equipping various devices and equipment with sensors and connecting to the Internet for remote monitoring and control of processes in real-time (including automatic mode). The Internet of Things, in particular, allows tracking the movement of goods, providing remote service, and supporting customer self-service.
21 Big Data Analytics involves processing and analyzing large databases to uncover patterns, trends, and insights that inform decision-making. Big Data is a technology that uses different approaches, tools, and methods for processing structured and unstructured data. Thanks to the automation of big data processing, BigData has a wide range of possibilities to combine and analyze industrial data on a certain aspect of activity. Therefore, using this technology, it is possible to predict and avoid large costs, identify risks for products or services, and reduce decision-making time. Often, Big Data is also used in neuromarketing, behavioral economics, and supply chain management.
22 Advanced technologies such as 5G are driving new applications in industries such as ultra-fast and low-latency autonomous vehicles, virtual reality (VR), and telemedicine.
23 Cloud computing, on the other hand, is meeting the demand for increased data storage and processing power, allowing businesses to manage operations more flexibly and cost-effectively. Cloud Computing (Cloud Computing) is an information technology concept that provides convenient network access based on user requests to the total volume of computing resources of various configurations that can be quickly provisioned and released with the lowest operating costs or without contacting providers. The results of digital transformation in the main economic sectors, where distributed computing technologies provide access to globally distributed resources using special tools and are used to solve resource-intensive economic problems, taking into account the characteristics and characteristics of each of them, show that the financial industry is implementing digital transformation through FinTech (Financial Technology). has been one of the fastest adopters. Innovations such as mobile banking, contactless payments, robo-advisors, and blockchain-based smart contracts have dramatically changed the way financial services are delivered. The indicated next-generation digital technologies are complemented by digital platforms. They include a set of applications that allow users to access a variety of services designed to plan, analyze, and provide information and communication with markets. Connections on digital platforms are established according to a special algorithm that reduces transaction costs and speeds up data exchange.
24

The basic principles of the formation of the new generation technological economy and the problems of its formation

The next generation economy is fundamentally reshaping the way value is created, distributed, and stored globally. Its formation is governed by a unique set of principles that reflect changing technological, social, and economic paradigms. These principles form the basis of a more flexible, innovative, and sustainable economic system adapted to the complexities of the 21st century. The rapid evolution of digital technology, and its integration into every area of society, has led to the emergence of what is now called the next-generation economy. This economic paradigm represents a transition from traditional models to new foundations driven by digital transformation, global connectivity, and sustainability [9, 10].
25 The next-generation economy is shaped not only by technological advances such as artificial intelligence, big data, blockchain, and automation but also by a deep commitment to promoting innovation, sustainability, and inclusion. Unlike previous economic frameworks that relied primarily on industrialization or service-based models, the next-generation economy thrives on a strong focus on data-driven insights, distributed systems, and collaborative ecosystems. It facilitates global cooperation and decentralized decision-making by being a dynamic system that seeks to balance environmental and social responsibility with technological progress.
26 In this context, it is very important to define the basic principles that guide the formation of the new generation economy. These principles include the introduction of innovation, the prioritization of sustainability and resource efficiency, digitization as a strategic resource, and the growing importance of data. These principles will create conditions for the development of strategies necessary to ensure the transformational potential of the economy and its prospective development. The main supporting principles of the formation of the new generation technological economy can be expressed as in Figure 1.
27

28 Fig. 1. The basic principles of the formation of a new generation technological economy (compiled by the authors)
29

Features and prospects of application of machine learning methods in forecasting prospective sectors of the new generation economy

The machine learning method was first introduced into the scientific literature in 1959 by A.Samuel. The main goal of machine learning is to teach computers to think and make decisions as humans do. This allows to implementation of automatic learning processes based on data analysis. Machine learning is widely used, especially in the field of artificial intelligence, and plays an important role in various application areas (for example, speech recognition, image processing, recommendation systems, financial analysis, etc.).
30 Classification and clustering are the basis of machine learning. Classification divides a set of objects into classes according to the signs of similarity and proximity. Regression - predicts the value of a given continuous value variable based on the values of other variables. Casterization is the process of dividing a given set of objects into groups by placing close objects in one cluster and distant objects in different clusters.
31

Since machine learning is a multidisciplinary field, it interacts with mathematical statistics, computer science, applied mathematics, psychology, physiology, etc. It is an interdisciplinary course that combines knowledge of probability theory, estimation theory, mathematics, and statistics. These methods are based on sophisticated algorithms that use computers as tools designed to mimic how humans learn in real time [2, 17]. The content of machine learning 1) Machine learning is an implementation of artificial intelligence that improves the efficiency of empirical learning algorithms. 2) Machine learning is the study of computer algorithms that can be automatically improved through experience. 3) Machine learning uses past data to improve the performance of computer programs. Data collection, data operations, data analysis, training, and testing stages are performed to solve problems with machine learning methods. Machine learning in Computer Vision, Speech Recognition, Image Recognition, Computer Linguistics and Natural Language Processing, Medical Diagnostics, Technical Diagnostics, Information Retrieval, etc. applies.
32

Machine learning is divided into several main categories: 1) Supervised learning. The model is presented with the correct answers beforehand. The system learns to respond correctly to new information by learning from these patterns. 2) Unsupervised learning. The model is given unanswered and uncertain data. The system tries to discover hidden patterns and structures among that data. 3) Reinforcement learning (Reinforcement Learning). The model learns based on experience. He receives grades in the form of rewards and punishments for certain decisions or actions. As a result, he learns to make good/correct decisions over time. This technology creates new opportunities in the world of science and business. Thus, it allows to analyze the data more accurately and make smart decisions using them. The classification features of machine learning methods can be expressed as in Figure 2.
33

34 Fig. 2. Classification characteristics of machine learning methods (compiled by the authors based on the analysis and systematization of scientific literature)
35

Machine learning models can be attributed to: The choice of the machine learning models as Logistic Regression (LR), K-Nearest Neighbor (KNN), Naive Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), and Artificial Neural Network (ANN), and ensemble methods as Random Forest (RF), and Adaptive Boosting (AdaBoost) are based on the most commonly used models in the reviewed works [20].
36

Decision trees (DT) method is used to predict discrete-valued objective functions. Here, a decision tree represents a learned function that combines predictor variables into an expected variable [19]. To search for the most discriminating variables, the decision tree algorithm uses a corresponding approach to construct a tree-like structure of nodes and edges. [19] used heuristic measures such as the Gini index, entropy, etc. in a research study on how the decision tree model works to determine the most discriminating variable values. Logistic regression is a nonlinear regression technique that associates a conditional probability score with each data sample. The essence of logistic regression is to examine the relationship between dependent and independent variables. The dependent variable can be binomial or multinomial.
37

About stages and methods of forecasting economic development

Economic forecasting shows the law of development of economic events and the relationship between various economic events, predicts future economic trends, and probabilities of economic events. The development stages of economic forecasting can be given as in Figure 3.
38 General principles should be followed in forecasting. So, first of all, it is necessary to determine what to predict. Then the product market trend forecast analysis should be done. At the same time, forecasting and analysis of capital requirements should also be done. A projected cost analysis should also be performed. Then, by a) gathering information, b) identification of data selection, v)analyzing, predicting, making judgments, and c) verifications should be checked. Analysis of forecasted sales and profit forecasting should be done. The results should also be checked and evaluated after they have been achieved.
39

40 Fig. 3. Stages of forecasting economic development (Compiled by the authors based on the analysis and systematization of scientific literature)
41

Machine learning methods applied in forecasting. A comparative analysis of machine learning methods applied in forecasting shows that when choosing them, some of the effective machine learning methods designed for the prediction of large amounts of data can be identified as in a separate table [22]. Their advantages and disadvantages are presented as in table 1.
42 Table 1. Comparison of machine learning methods used in forecasting
The name of the method Advantages Disadvantages
Random Forest
  • processing of non-linear dependencies;
  • determining the importance of various factors;
  • resilience of repeated learning
 • labor capacity of the learning; • difficulty of interpretations;
  • tendency to adapt to retraining when there is a large number of trees
Gradient Boosting
  • processing of non-linear dependencies;
  • determining the importance of factors;
  • resistance to emissions;
  • high accuracy;
  • internal cross-validation;
  • processing missing values;
  • parallelism of calculations
 • labor capacity of the learning; • difficulty of interpretations;
  • tendency to adapt to a large number of iterations
Neural networks
  • processing of non-linear dependencies;
  • determining the importance of factors;
  • resistance to emissions;
  • universality of use;
  • dynamism of parameters
 • labor capacity of the learning; • difficulty of interpretations; • tendency to adapt to a large number of iterations; • demand for large amounts of data; • complexity of calculations;
43

Stages of application of the machine learning method in the forecasting process

Machine learning is an artificial intelligence approach that allows models to make predictions based on previous observations [11]. Machine learning is used to power AI applications (figure 4). Building effective AI applications based on machine learning is complex. The steps to be performed include data preparation, characterization, model selection, parameter optimization, validation, etc.
44

45 Fig. 4. Stages of application of the machine learning method in the forecasting process (Compiled by the authors based on the analysis and systematization of scientific literature)
46

Conclusion

In modern times, the rapid development of the economy based on the application of digital transformations and technological innovations is considered one of the urgent issues. Forming prospective sectors of the new generation economy on the basis of digital technologies such as artificial intelligence and robotization, bio, nano, information-communication, space, etc., and the effective application of machine learning methods in its forecasting is considered one of the main ways to achieve faster development.
47 The application of machine learning methods in predicting the formation of prospective sectors of the next-generation economy is of particular importance in the rapidly developing digital and technological ecosystem. Forecasting the formation of new sectors in the economy is one of the issues implemented to plan the development strategies of countries. The application of machine learning methods is considered important in increasing the efficiency of making such forecasts more accurate and stable. Machine learning algorithms, being one of the main methods in the analysis and modeling of large volumes of data, have caused revolutionary changes in various fields of the economy. Machine learning technologies have many analytical capabilities to identify promising sectors of the economy. Neural Networks, Random Forest, etc. used in forecasting work. It is possible to make strategic decisions by identifying potential development areas in the future through deep training methods. Currently, the machine learning method is used to improve the relations between different sectors of the economy, the efficiency of industrial production, investment dynamics, etc. used to predict. It helps to study changing trends in various sectors of the economy and predict the emergence of new business models and services. Research results on the application of machine learning methods in predicting the formation of prospective sectors of the new generation economy can provide significant results in several main directions. These results include new opportunities such as the integration of technology into various sectors of the economy, pre-identification of developing areas and efficient use of resources, identification of prospective sectors, increasing the accuracy of forecasts, optimal allocation and effective management of resources, assessment of risks and opportunities, study of favorable market conditions, etc. will create.
48 Scientific research shows that the application of machine learning methods to promising sectors of the next-generation economy will create a strong basis for sustainable economic growth in the future. By using the powerful analytical capabilities of technology, competitive advantage, and sustainability can be increased, ensuring sustainable and flexible development of the economy. Future research and development directions conducted in this direction provide new opportunities for further deepening of technologies in economic analysis and decision-making processes. Some of the areas that are important to pay attention to in future scientific research include the application of deep learning and machine learning models, Improving data quality, Creating decision support systems with machine learning in the analysis of economic indicators, Creating adaptive and dynamic forecasting systems, etc. Solving the issues that arise as a result of the application and providing socially responsible approaches in the development of the economy, as well as predicting climate change and sustainable development sectors are among the dynamic application areas of machine learning methods.
49 The conducted studies show that the processes of developing or improving the directions for solving the problems of applying machine learning methods in predicting the formation of prospective sectors of the new generation economy should be dynamic in nature.
50 Contributions. Increasing the level of application of machine learning methods in predicting the formation of prospective sectors of the new generation economy can give a serious impetus to sustainable development and increase the stability of the new generation technological economy. Accordingly, relevant recommendations and proposals developed in this field should be taken into account. The main contributions include increasing the accuracy of economic forecasting, supporting strategic decision-making, identifying opportunities in the field of digital and green economy, effectively managing economic risks, acquiring new knowledge in the field of research and teaching, offering innovative economic models, etc. The wide application of these methods lays the groundwork for further modernization of the economy and for technologies to play a strategic role in economic development.
51 The usefulness of the obtained result and application in practice. The application of machine learning methods in predicting the formation of prospective sectors of the new generation economy, the review of the solution to the problems of raising the level of its effective management, as well as their prospective development directions can be applied in the development of other regional enterprises, in the development of solution mechanisms and options. The analysis of the results of the application of machine learning methods in predicting the formation of prospective sectors of the new generation economy can serve as a platform for predicting the activity of other economic sectors in general. The application of such methods provides a basis for processing and making appropriate management decisions. The proposed methodological and conceptual approach to effective management of the formation of prospective sectors of the new generation economy based on the application of machine learning methods can be applied to other regional technological economy sectors.

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