RAQAMLI IQTISODIYOT SHAROITIDA STATISTIK MA’LUMOTLARNI SUN’IY INTELLEKT ASOSIDA TAHLIL QILISH IMKONIYATLARI
DOI:
https://doi.org/10.5281/zenodo.21037204Keywords:
raqamli iqtisodiyot, sun’iy intellekt, statistik tahlil, mashinaviy o‘qitish, katta ma’lumotlar, bashoratli tahlil, neyron tarmoq, davlat statistikasiAbstract
Ushbu maqolada raqamli iqtisodiyot sharoitida statistik ma’lumotlarni sun’iy intellekt (SI)
texnologiyalari asosida tahlil qilish imkoniyatlari nazariy va amaliy jihatdan o‘rganilgan. Mualliflar an’anaviy
statistik usullarning katta hajmli, tezkor va xilma-xil ma’lumotlar (Big Data) bilan ishlashdagi cheklovlarini aniqlab,
mashinaviy o‘qitish (ML) va chuqur o‘qitish (DL) modellarining tavsifiy, bashoratli hamda yo‘naltiruvchi tahlildagi
ustunliklarini ko‘rsatgan. Qiyosiy tahlil natijalariga ko‘ra, SI asosidagi modellar talab prognozi, anomaliyalarni
aniqlash, segmentlash va matnli ma’lumotlar tahlilida aniqlikni o‘rtacha 17–28 foiz bandga oshiradi. Maqolada
O‘zbekiston Respublikasining raqamli iqtisodiyotni rivojlantirish siyosati kontekstida davlat statistikasini
intellektuallashtirishning ustuvor yo‘nalishlari va asosiy muammolari (ma’lumotlar sifati, interpretatsiya, kadrlar
va infratuzilma) muhokama qilingan.
References
1. O‘zbekiston Respublikasi Prezidentining 2020-yil 5-oktabrdagi “Raqamli O‘zbekiston —
2030” strategiyasini tasdiqlash to‘g‘risida PF-6079-son Farmoni. https://lex.uz/docs/-
5030957?ONDATE=12.08.2021
2. Hastie T., Tibshirani R., Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and
Prediction. 2nd ed. — New York: Springer, 2009. — 745 p.
3. James G., Witten D., Hastie T., Tibshirani R. An Introduction to Statistical Learning with Applications in R.
2nd ed. — New York: Springer, 2021. — 607 p.
4. Goodfellow I., Bengio Y., Courville A. Deep Learning. — Cambridge: MIT Press, 2016. — 800 p.
5. Bishop C. M. Pattern Recognition and Machine Learning. — New York: Springer, 2006. — 738 p.
6. Provost F., Fawcett T. Data Science for Business: What You Need to Know about Data Mining and Data-
Analytic Thinking. — Sebastopol: O’Reilly Media, 2013. — 414 p.
7. Chen H., Chiang R. H. L., Storey V. C. Business Intelligence and Analytics: From Big Data to Big Impact //
MIS Quarterly. — 2012. — Vol. 36, No. 4. — P. 1165–1188.
8. Varian H. R. Big Data: New Tricks for Econometrics // Journal of Economic Perspectives. — 2014. — Vol.
28, No. 2. — P. 3–28.
9. Mullainathan S., Spiess J. Machine Learning: An Applied Econometric Approach // Journal of Economic
Perspectives. — 2017. — Vol. 31, No. 2. — P. 87–106.
10. Athey S., Imbens G. W. Machine Learning Methods That Economists Should Know About // Annual Review
of Economics. — 2019. — Vol. 11. — P. 685–725.
11. Lundberg S. M., Lee S.-I. A Unified Approach to Interpreting Model Predictions // Advances in Neural
Information Processing Systems (NeurIPS). — 2017. — P. 4765–4774.
12. Ribeiro M. T., Singh S., Guestrin C. “Why Should I Trust You?”: Explaining the Predictions of Any Classifier
// Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data
Mining. — 2016. — P. 1135–1144.
13. Brynjolfsson E., McElheran K. The Rapid Adoption of Data-Driven Decision-Making // American Economic
Review. — 2016. — Vol. 106, No. 5. — P. 133–139.
14. OECD. Artificial Intelligence in Society. — Paris: OECD Publishing, 2019. — 152 p.
15. United Nations Economic Commission for Europe (UNECE). Machine Learning for Official Statistics. —
Geneva: United Nations, 2022. — 96 p.
16. Hyndman R. J., Athanasopoulos G. Forecasting: Principles and Practice. 3rd ed. — Melbourne: OTexts,
2021. — 442 p.
17. G‘aniyev B. S., Karimov N. R. Raqamli iqtisodiyotda ma’lumotlarni tahlil qilishning zamonaviy usullari //
Iqtisodiyot va innovatsion texnologiyalar. — 2022. — № 4. — B. 58–67.
18. Abdullayev A. M. Sun’iy intellekt texnologiyalarining iqtisodiy tahlildagi o‘rni // O‘zbekiston statistika
axborotnomasi. — 2023. — № 2. — B. 21–30.
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