Design and creation of an information system for analytical data processing

Kovalchuk, M.L.; Lucin, P.; Gorsky, M.P.; Soltys, I.V.

doi:10.31649/1681-7893-2022-44-2-26-31

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…Methods of approximating and modeling stock indices using a Wiener random process serve as the basis for intelligent data analysis using machine learning. The main goal of this analysis is to predict the price of the test data depending on real quotes over time, taking into account the errors of real prediction [6,7], [8,9], [10,11], [12,13], [14,15], [16,17], [18,19], [20].…”

Section: Research Resultsmentioning

confidence: 99%

Modeling and forecasting of stock market processes

Uhryn,

Karachevtsev,

Shevchuk

et al. 2024

HAIT

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Stock market valuation uses a variety of indicators, such as indices and ratings, to reflect its state and movement. For example, a stock exchange index reflects activity on a stock exchange and is calculated using specific formulas. The calculation of indices is based on statistical data on securities and helps to assess the risks of investments. These indices reflect market conditions. The methodology for forming stock indices includes four stages: sampling, weighting of shares, calculation of the average, and conversion to the index form. Two types of sampling are used: deterministic and floating-power sampling. The weighting coefficients are determined by the price criterion and market capitalization. The studied approaches to stock market modeling allow identifying functional dependencies in the data and developing forecasts. In particular, the methods of approximation and modeling by the Wiener process are allocated. Stock market forecasting using the multi-layer architecture of Long Short-Term Memory in the Keras library is investigated. The overall results confirm that an intelligent information system for automated trading decisions is effective, providing traders with competitive advantages and reducing risks.

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Section: Research Resultsmentioning

confidence: 99%

Modeling and forecasting of stock market processes

Uhryn,

Karachevtsev,

Shevchuk

et al. 2024

HAIT

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“…The information received from users from the web platform contains various types of uncertainties due to various "nonfactors". To take into account the uncertainties of the initial information for analyzing the web platform, it is proposed to use fuzzy logic in [12,13], [29,30].…”

Section: Fig 1 Classification Of Comments In Web Platformsmentioning

confidence: 99%

Information system for analyzing publicsentiment in web platforms based on machine learning

Uhryn,

Karachevtsev,

Tomka

et al. 2024

HAIT

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The systems for studying public sentiment in web platforms are analyzed. Various tools and methods for effectively determining the mood in textual data from web platforms are described, including the formalization of the social graph and the content graph.The process of classifying comments, which includes the systematization and categorization of statements, is investigated. Based on the studied dataset, information on customer reviews and hotel ratings in Europe from the booking.com web platform is selected. Taking into account the requirements of the information system and the results of the analysis, it is determined that in order to obtain better results in determining the emotional connotation of the texts of reviews and messages from users, the most appropriate is the use of machine learning methods, taking into account natural language methods for processing text data. When choosing a text vectorization method for machine learning, the Term Frequency Inverse Document Frequency Vectorizer was chosen as the most effective among the studied methods. The architectural structure of the studied system is proposed, which is aimed at effective interaction between components and modules. The LogisticRegression model is chosen to determine the publicmood. An information system has been developed that analyzes public sentiment about objects, uses advanced machine learning technologies to assess the emotional connotation of text comments, and provides users with insights and analysis of the results.

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Design and creation of an information system for analytical data processing

Cited by 2 publications

References 4 publications

Modeling and forecasting of stock market processes

Modeling and forecasting of stock market processes

Information system for analyzing publicsentiment in web platforms based on machine learning

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