ADVANTAGES OF FORECASTING ECONOMIC PROCESSES USING THE ARIMA MODEL IN PYTHON
Keywords:
Python, ARIMA model, econometric modeling, forecasting, time series, wheat, agriculture, statsmodels.Abstract
This thesis highlights the advantages of forecasting economic processes using the AutoRegressive Integrated Moving Average (ARIMA) model implemented in the Python programming language. The empirical part of the study uses official statistical data on wheat production in the Surkhandarya Region for 2010–2024.
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References
1. Box G. E. P., Jenkins G. M., Reinsel G. C., Ljung G. M. Time Series Analysis: Forecasting and Control. 5th Edition. John Wiley & Sons, 2015.
2. Hyndman R. J., Athanasopoulos G. Forecasting: Principles and Practice. 3rd Edition. OTexts, 2021.
3. Hamilton J. D. Time Series Analysis. Princeton University Press, 1994.
4. McKinney W. Python for Data Analysis. 3rd Edition. O'Reilly Media, 2022.
5. Seabold S., Perktold J. Statsmodels: Econometric and Statistical Modeling with Python. Proceedings of the 9th Python in Science Conference, 2010.
