Abstract
Mazkur tezisda Python dasturlash tilida vaqt qatorlarini prognozlashda keng qo‘llaniladigan ARIMA, SARIMA va Prophet modellarining imkoniyatlari qiyosiy tahlil qilindi. Tadqiqotda iqtisodiy ko‘rsatkichlar vaqt qatori ma'lumotlari asosida uchala model qurildi va ularning prognozlash aniqligi, hisoblash tezligi, qo‘llash qulayligi hamda amaliy afzalliklari baholandi. Modellashtirish jarayonida Python dasturining pandas, statsmodels, prophet va matplotlib kutubxonalaridan foydalanildi. Tadqiqot natijalari shuni ko‘rsatdiki, ARIMA modeli oddiy vaqt qatorlari uchun, SARIMA mavsumiy o‘zgarishlarga ega qatorlar uchun, Prophet modeli esa murakkab trend va mavsumiylik kuzatiladigan iqtisodiy jarayonlar uchun samarali natijalar beradi.
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.

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