O‘ZBEKISTON HUDUDLARI IQTISODIY TAFOVUTLARINI FAZOVIY SUN’IY INTELLEKT ASOSIDA KAMAYTIRISHNING DIFFERENSIAL MEXANIZMI

Mahliyo  Bobomurodova

Mualliflar

Mahliyo Bobomurodova Muallif

{$ Etel}:

hududiy tafovut, fazoviy sun’iy intellekt, graf neyron tarmoq, GNN, XGBoost, Teyl indeksi, Moran I, fazoviy diffuziya, differensial siyosat, gibrid model.

Abstrak

Maqolada O‘zbekistonning 14 ta hududi bo‘yicha 2016–2024-yillarning choraklik rasmiy statistikasi asosida hududlararo iqtisodiy tafovutlarni kamaytirishning differensial mexanizmi ishlab chiqilgan. Fazoviy avtokorrelyatsiya tahlili bog‘liqlik geografik qo‘shnichilik emas, iqtisodiy tuzilmaviy o‘xshashlik orqali namoyon bo‘lishini ko‘rsatdi. Fazoviy-vaqtinchalik graf neyron tarmog‘i (ST-GNN) va XGBoost gibridi asosida har bir hududga beriladigan impulsning tafovutga ta’siri to‘g‘ridan-to‘g‘ri va diffuziya qismlariga ajratilib, hududlarning ustuvorlik reytingi tuzilgan.

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Bibliografik havolalar

[1] O‘zbekiston Respublikasi Prezidenti, “O‘zbekiston – 2030 strategiyasi to‘g‘risida,”O‘zbekiston Respublikasi Prezidentining PF-158-son Farmoni, Sep. 11, 2023. [Online]. Available: https://lex.uz/docs/-6600413

[2] J. G. Williamson, “Regional inequality and the process of national development,”Economic Development and Cultural Change, vol. 13, no. 4, pp. 1–84, 1965.

[3] P. Krugman, “Increasing returns and economic geography,” Journal of Political Economy, vol. 99, no. 3, pp. 483–499, 1991.

[4] R. J. Barro and X. Sala-i-Martin, “Convergence,” Journal of Political Economy, vol. 100, no. 2, pp. 223–251, 1992.

[5] H. Theil, Economics and Information Theory. Amsterdam, The Netherlands: North-Holland Publishing Company, 1967, 488 p.

[6] P. A. P. Moran, “Notes on continuous stochastic phenomena,” Biometrika, vol. 37, nos. 1–2, pp. 17–23, 1950.

[7] L. Anselin, Spatial Econometrics: Methods and Models. Dordrecht, The Netherlands: Kluwer Academic Publishers, 1988, 284 p.

[8] J. LeSage and R. K. Pace, Introduction to Spatial Econometrics. Boca Raton, FL, USA: CRC Press, 2009, 374 p.

[9] J. P. Elhorst, Spatial Econometrics: From Cross-Sectional Data to Spatial Panels. Heidelberg, Germany: Springer, 2014, 119 p.

[10] T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in Proc. International Conference on Learning Representations (ICLR), 2017.

[11] W. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in Advances in Neural Information Processing Systems 30, 2017, pp. 1024–1034.

[12] B. Yu, H. Yin, and Z. Zhu, “Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting,” in Proc. IJCAI-18, 2018, pp. 3634–3640.

[13] L. Zhao et al., “T-GCN: A temporal graph convolutional network for traffic prediction,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 9, pp. 3848–3858, 2020.

[14] Q. Li, Z. Han, and X.-M. Wu, “Deeper insights into graph convolutional networks for semi-supervised learning,” in Proc. AAAI-18, 2018, pp. 3538–3545.

[15] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997.

[16] T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 785–794.

[17] S. Makridakis, E. Spiliotis, and V. Assimakopoulos, “The M4 competition: 100,000 time series and 61 forecasting methods,” International Journal of Forecasting, vol. 36, no. 1, pp. 54–74, 2020.

[18] R. J. Hyndman and A. B. Koehler, “Another look at measures of forecast accuracy,” International Journal of Forecasting, vol. 22, no. 4, pp. 679–688, 2006.

[19] F. X. Diebold and R. S. Mariano, “Comparing predictive accuracy,” Journal of Business & Economic Statistics, vol. 13, no. 3, pp. 253–263, 1995.

[20] F. Pedregosa et al., “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.

[21] A. Paszke et al., “PyTorch: An imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems 32, 2019, pp. 8024–8035.

[22] Statistics Agency under the President of the Republic of Uzbekistan, “Open data (SDMX): Gross regional product (quarterly) and resident population,” 2026. [Online]. Available: SIAT Open Data Portal. Accessed: Sep. 9, 2026.