Перегляд за Автор "Baran, D."

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  • Ескіз
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    Algorithms of statistical anomalies clearing for data science applications
    (КПІ ім. Ігоря Сікорського, 2023) Pysarchuk, O.; Baran, D.; Mironov, Yu.; Pysarchuk, I.
    The paper considers the nature of input data used by Data Science algorithms of modern-day application domains. It then proposes three algorithms designed to remove statistical anomalies from datasets as a part of the Data Science pipeline. The main advantages of given algorithms are their relative simplicity and a small number of configurable parameters. Parameters are determined by machine learning with respect to the properties of input data. These algorithms are flexible and have no strict dependency on the nature and origin of data. The efficiency of the proposed approaches is verified with a modeling experiment conducted using algorithms implemented in Python. The results are illustrated with plots built using raw and processed datasets. The algorithms application is analyzed, and results are compared.
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    Multi-factor forecasting of statistical trends for Data Science problems
    (КПІ ім. Ігоря Сікорського, 2024) Pysarchuk, O.; Andreieva, T.; Grinenko, O.; Baran, D.
    The article deals with the processes of multi-factor forecasting of statistical trends for Data Science problems. Most of the classic approaches to data processing consist of studying the consequences of phenomena rather than the factors of their appearance. At the same time, the factors affecting the behavior of the investigated process are assumed to be random and are not investigated. The article discusses the approach to forecasting the parameters of the trend of statistical time series, which consists of the study of factors that lead to changes in the dynamics of the studied process. This approach potentially has better indicators of adequacy, accuracy, and efficiency in obtaining final solutions than classical approaches. The implementation of this approach is shown using an example of the analysis of exchange rate changes. The obtained results show the practicality of considering multifactoriality in forecasting tasks.