ARIMA (AUTOREGRESSIVE INTEGRATED MOVING AVERAGE) MODELI ASOSIDA O‘ZBEKISTON YAIM KO‘RSATKICHINI BASHORAT QILISH

Mualliflar

  • Ahmadxon Azibayev

Kalit so'zlar

https://doi.org/10.47390/ts-v3i5y2025N5

Kalit so'zlar

YaIM prognozi, ARIMA modeli, O‘zbekiston iqtisodiyoti, iqtisodiy siyosat, vaqt qatori tahlili, iqtisodiy rejalashtirish

Annotasiya

Ushbu maqolada O‘zbekiston yalpi ichki mahsuloti (YaIM)ni 2025–2030-yillar davri uchun prognoz qilishda ARIMA (Autoregressive Integrated Moving Average) modelidan foydalanilgan. YaIM – bu mamlakat ichida ishlab chiqarilgan barcha mahsulot va xizmatlarning pulda ifodalangan umumiy qiymatini aks ettiruvchi muhim iqtisodiy ko‘rsatkichdir. Tadqiqotda YaIMni prognoz qilish siyosiy qarorlar qabul qilish, resurslarni taqsimlash va investitsiya rejalashtirishda katta ahamiyat kasb etishi ta’kidlanadi. Natijalar O‘zbekiston YaIMida umumiy o‘sish tendensiyasi mavjudligini, biroq ba’zan nomuntazam o‘zgarishlar kuzatilishini ko‘rsatadi. ARIMA modeli prognozlashda yuqori aniqlikka ega bo‘lib, tarixiy ma’lumotlar va hozirgi iqtisodiy islohotlar bilan mos keladi. Shu bilan birga, tadqiqotda tashqi omillarni ham inobatga oluvchi gibrid yondashuvlar yordamida modelni yanada takomillashtirish imkoniyati mavjudligi ko‘rsatilgan

Manbalar

1. Hanke, J. E., & Wichern, D. W. (2009). Business forecasting 9th ed. New Jersey.

2. Diebold, F. X. (1998). The past, present, and future of macroeconomic forecasting. Journal of Economic Perspectives, 12(2), 175-192.

3. Uddin, K., & Tanzim, N. (2021). Forecasting GDP of Bangladesh using ARIMA model. International Journal of Business and Management, 16(6), 56-65.

4. Abonazel, M. R., & Abd-Elftah, A. I. (2019). Forecasting Egyptian GDP using ARIMA models. Reports on Economics and Finance, 5(1), 35-47.

5. Musundi, S. W., M’mukiira, P. M., & Mungai, F. (2016). Modeling and forecasting Kenyan GDP using autoregressive integrated moving average (ARIMA) models.

6. Atanu, E. Y., Ette, H. E., Nwuju, K., & Nwaoha, W. C. (2020). ARIMA Model for gross domestic product (GDP): evidence from Nigeria. Archives of Current Research International, 20(7), 49-61.

7. Azibaev, A. (2024). FORECASTING GDP GROWTH AND GDP PER CAPITA IN UZBEKISTAN BY THE ORDINARY LEAST SQUARES (OLS) REGRESSION ANALYSIS. Scientific and Technical Journal of Namangan Institute of Engineering and Technology, 9(2), 284-290.

8. Otto, M., & Thornton, J. (2023). Forecasting gross domestic product (gdp) and gdp growth: an exploration of improved prediction using machine learning algorithms. Qo‘qon universiteti xabarnomasi, 9-14.

9. Ugli, A. A. G. (2024). Analytical and numerical expressions of the golden rule of capital accumulation. Илм-фан ва инновацион ривожланиш/Наука и инновационное развитие, 7(4), 15-26.

10. Akhmadkhon, A. (2025). THE ROLE OF GRADIENT BOOSTING MACHINES IN MODERN ECONOMIC ANALYSIS. Universum: технические науки, 6(1 (130)), 11-14.

11. Pandey, M. C., & Rawat, P. S. (2024). Virtual Machine Provisioning Within Data Center Host Machines Using Ensemble Model in Cloud Computing Environment. SN Computer Science, 5(6), 690.

12. www.coursehero.com

13. www.researchgate.net

14. www.askyourdata.co

15. www.machinelearningplus.com

16. www.coursehero.com

##submission.downloads##

Yuborilgan

2025-08-10

Nashr qilingan

2025-08-11

Qanday ko'rsatish

Azibayev , A. (2025). ARIMA (AUTOREGRESSIVE INTEGRATED MOVING AVERAGE) MODELI ASOSIDA O‘ZBEKISTON YAIM KO‘RSATKICHINI BASHORAT QILISH. Techscience Uz - Topical Issues of Technical Sciences, 3(5), 30–35. https://doi.org/10.47390/ts-v3i5y2025N5

##plugins.generic.recommendBySimilarity.heading##

1 2 3 4 > >> 

##plugins.generic.recommendBySimilarity.advancedSearchIntro##