MODELING ARIMA AND LSTM TIME SERIES MODELS IN PYTHON
DOI:
https://doi.org/10.30888/2663-5712.2025-30-01-003Keywords:
time series analysis, machine learning, neural networksAbstract
This article examines the theoretical foundations and practical aspects of applying ARIMA models, RNN architecture, and the working principles of LSTM, as well as their use for time series forecasting in Python using the Statsmodels and Keras libraries.Metrics
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References
Тарасов М. С. Порівняння ARIMA та LSTM моделей часових рядів / Кваліфікаційна робота.- ЧНУ. – 2024. – 66 с.
Brownlee J. Introduction to Time Series Forecasting with Python. — 1st ed. — 2020. — 365 p.
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2025-03-30
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Дорошенко, І., & Тарасов, М. (2025). MODELING ARIMA AND LSTM TIME SERIES MODELS IN PYTHON. SWorldJournal, 1(30-01), 72–81. https://doi.org/10.30888/2663-5712.2025-30-01-003
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