COMPARATIVE ANALYSIS OF MODERN TIME SERIES FORECASTING METHODS FOR SOLVING PRACTICAL PROBLEMS

Authors

DOI:

https://doi.org/10.30888/2663-5712.2026-35-03-015

Keywords:

machine learning, model, neural networks, forecasting, time series, ARIMA, ARIMAX, DEEPAR, PROPHET, SARIMA, SARIMAX

Abstract

The goal of this work is to develop a modified technology for error detection in text documents using a multilayer perceptron neural network. Each of the considered forecasting models has its own advantages and limitations when using them for the selected

References

Kholiavka Y., & Parfenenko Y. (2023). Forecasting peak load on the power grid. Computer Systems and Information Technologies, (3), 12–22. https://doi.org/10.31891/csit-2023-3-2

Hovorushchenko T, Medzatyi D, Voichur Y, Lebiga M. (2023). Method for forecasting the level of software quality based on quality attributes. 2023, Journal of Intelligent & Fuzzy Systems, 1-15. https://doi.org/10.3233/JIFS-222394.

Hyndman R.J., & Athanasopoulos G. (2018) Forecasting: principles and practice, 2nd edition, Texts: Melbourne, Australia. URL: https://otexts.com/fpp2/expsmooth.html.

David S, Valentin F, Jan G. (2019). DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks. arXiv 2019. arXiv https://doi.org/10.48550/arXiv.1704.04110.

Zahra F, Minh H, Elena Z, Zamir S, Xiaojun D. (2023). Mitigating Cold-start Forecasting using Cold Causal Demand Forecasting Model. arXiv 2023. https://doi.org/10.48550/arXiv.2306.09261.

Published

2026-01-30

How to Cite

Сумець, С., Удовенко, С., Шергін, В., & Чала, Л. (2026). COMPARATIVE ANALYSIS OF MODERN TIME SERIES FORECASTING METHODS FOR SOLVING PRACTICAL PROBLEMS. SWorldJournal, 3(35-03), 34–47. https://doi.org/10.30888/2663-5712.2026-35-03-015

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