Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций
Конференция: International Conference on IT in Business and Industry, ITBI 2021
Год издания: 2021
Идентификатор DOI: 10.1088/1742-6596/2032/1/012031
Аннотация: In this article, various approaches to the classification of time series are described and investigated in practice. This research paper discusses the operations of time series analysis using neural networks in order to improve time series, making them more accurate and fast. Also, the authors conducted a study and comparison of seПоказать полностьюveral metrics of various classifiers: “Method of k-nearest neighbours”, “Tree solutions” and “Random forest”. The following parameters were chosen as metrics for evaluating existing algorithms: "Accuracy", "Average absolute error", “F-measure”. For all methods, a single dataset was selected and experiments were performed based on this single dataset, which allows an objective assessment of the work of classifiers in relation to the binary classification. We used the Python programming language with the main library SKlearn, the Ipythonnotebook application for implementing the program code as means for research in this work. It was found that there was a direct relationship between the time spent on training/testing the model and the prediction accuracy of the classifier. © 2021 Institute of Physics Publishing. All rights reserved.
Журнал: Journal of Physics: Conference Series
Выпуск журнала: Vol. 2032, Is. 1
Номера страниц: 12031
ISSN журнала: 17426588
Издатель: IOP Publishing Ltd