MODELING ARIMA AND LSTM TIME SERIES MODELS IN PYTHON

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DOI:

https://doi.org/10.30888/2663-5712.2025-30-01-003

Keywords:

time series analysis, machine learning, neural networks

Abstract

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.

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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.

Published

2025-03-30

How to Cite

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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Articles