O‘zbekistonda iqtisodiy jarayonlarni tadqiq etishda “machine learning” modellaridan foydalanish masalalari
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Опубликован:
Jun 26, 2020
License:
Это произведение доступно по лицензии Creative Commons «Attribution-ShareAlike» («Атрибуция — На тех же условиях») 4.0 Всемирная
Это произведение доступно по лицензии Creative Commons «Attribution-ShareAlike» («Атрибуция — На тех же условиях») 4.0 Всемирная
Раздел
Статьи
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Аннотация
Ushbu tezis O'zbekiston iqtisodiyoti uchun makroiqtisodiy prognozlashda machine learning ayrim metodlari, jumladan, LASSO, Ridge, Random Forest, Gradient Boosting va Sun’iy Neyron tarmoqlarini ishlatishni taklif qiladi. Ushbu ilmiy ish makroiqtisodiy prognozlashda machine learning usullarini O‘zbekiston iqtisodiyoti uchun qo'llash bo'yicha dastlabki urinishlardan biridir. Ushbu ishning asosiy natijasi – machine learning usullaridan foydalangan holda, O'zbekistonda iqtisodiy ko'rsatkichlarni yanada aniq prognozlash imkoniyati mavjudligini ko‘rsatish
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Как цитировать
Hakimov, H. (2020). O‘zbekistonda iqtisodiy jarayonlarni tadqiq etishda “machine learning” modellaridan foydalanish masalalari. Архив научных исследований, 1(11). извлечено от https://ejournal.tsue.uz/index.php/archive/article/view/1544
Литература
1. Phillips Curve Inflation Forecasts James H. Stock, Mark W. Watson NBER Working Paper No. 14322 September 2008
2. Inflation forecasting; Faust, Jon; Jonathan H. Wright; Handbook of Forecasting, 2:A, 2013, G. Elliott and A. Timmermann eds., 2013, 2-56.)
3. Fokin, N. and Polbin, A. (2019). Forecasting Russia's Key Macroeconomic Indicators with the VAR-LASSO Model. Russian Journal of Money and Finance, 78(2), pp. 67–93.
4. Yun Liao, 2017. "Machine Learning in Macro-Economic Series Forecasting," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 9(12), pages 71-76, December.
5. Siami-Namini, Sima et al. “A Comparison of ARIMA and LSTM in Forecasting Time Series.” 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) (2018): 1394-1401.
6. Nonlinear Inflation Forecasting with Recurrent Neural Networks Anna Almosova Niek Andresen (2019)
7. Hakimov, Hakimjon. 2020. Issues of optimic basis of public financing in following financial and monetary policy. https://journal.tsue.uz/index.php/archive/article/view/927
8. Ҳакимов, Ҳакимжон. 2019. «Макроиқтисодий барқарорликни таъминлашда фискал ва монетар сиёсатни мувофиқлаштириш зарурати». https://journal.tsue.uz/index.php/archive/article/view/627
9. Махмудов, Носир; Ҳакимов, Ҳакимжон. 2017. «Макроиқтисодий барқарорликни мустаҳкамлаш- барқарор иқтисодий ўсишни таъминлашнинг муҳим шарти». https://journal.tsue.uz/index.php/archive/article/view/674.
10. Баҳриддинов, В.; Ҳакимов, Ҳ. (2018). Инфляцияни таргетлаш сиёсатини амалга оширишнинг хориж тажрибаси. https://journal.tsue.uz/index.php/archive/article/view/666
11. Ҳакимов, Ҳакимжон. 2018. «Мамлакатда фискал ва монетар сиёсатни мувофиқлаштиришнинг моҳияти ва сабаблари». https://journal.tsue.uz/index.php/archive/article/view/224.
12. Махмудов Н.М, Ҳакимов Ҳ.А. "Иқтисодий ўсиш моделлари ва улардан макроиқтисодий таҳлилда фойдаланиш йўналишлари." Экономика и финансы (Узбекистан) 3 (2016).
13. Хакимов, Х. А. (2016). Роль государства в обеспечении макроэкономической стабилности. Ученый XXI века, (5-2)
14. Ҳакимов, Ҳ. (2020). Инқироз ва ундан кейинги шароитлар учун давлат қарзларининг юқори чегараси. https://journal.tsue.uz/index.php/archive/article/view/1038
15. Баҳриддинов, В. (2019). Инфляцион таргетлаш сиёсатинининг хусусиятлари ҳамда уни ўзбекистонда жорий этиш истиқболлари. https://journal.tsue.uz/index.php/archive/article/view/1079
2. Inflation forecasting; Faust, Jon; Jonathan H. Wright; Handbook of Forecasting, 2:A, 2013, G. Elliott and A. Timmermann eds., 2013, 2-56.)
3. Fokin, N. and Polbin, A. (2019). Forecasting Russia's Key Macroeconomic Indicators with the VAR-LASSO Model. Russian Journal of Money and Finance, 78(2), pp. 67–93.
4. Yun Liao, 2017. "Machine Learning in Macro-Economic Series Forecasting," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 9(12), pages 71-76, December.
5. Siami-Namini, Sima et al. “A Comparison of ARIMA and LSTM in Forecasting Time Series.” 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) (2018): 1394-1401.
6. Nonlinear Inflation Forecasting with Recurrent Neural Networks Anna Almosova Niek Andresen (2019)
7. Hakimov, Hakimjon. 2020. Issues of optimic basis of public financing in following financial and monetary policy. https://journal.tsue.uz/index.php/archive/article/view/927
8. Ҳакимов, Ҳакимжон. 2019. «Макроиқтисодий барқарорликни таъминлашда фискал ва монетар сиёсатни мувофиқлаштириш зарурати». https://journal.tsue.uz/index.php/archive/article/view/627
9. Махмудов, Носир; Ҳакимов, Ҳакимжон. 2017. «Макроиқтисодий барқарорликни мустаҳкамлаш- барқарор иқтисодий ўсишни таъминлашнинг муҳим шарти». https://journal.tsue.uz/index.php/archive/article/view/674.
10. Баҳриддинов, В.; Ҳакимов, Ҳ. (2018). Инфляцияни таргетлаш сиёсатини амалга оширишнинг хориж тажрибаси. https://journal.tsue.uz/index.php/archive/article/view/666
11. Ҳакимов, Ҳакимжон. 2018. «Мамлакатда фискал ва монетар сиёсатни мувофиқлаштиришнинг моҳияти ва сабаблари». https://journal.tsue.uz/index.php/archive/article/view/224.
12. Махмудов Н.М, Ҳакимов Ҳ.А. "Иқтисодий ўсиш моделлари ва улардан макроиқтисодий таҳлилда фойдаланиш йўналишлари." Экономика и финансы (Узбекистан) 3 (2016).
13. Хакимов, Х. А. (2016). Роль государства в обеспечении макроэкономической стабилности. Ученый XXI века, (5-2)
14. Ҳакимов, Ҳ. (2020). Инқироз ва ундан кейинги шароитлар учун давлат қарзларининг юқори чегараси. https://journal.tsue.uz/index.php/archive/article/view/1038
15. Баҳриддинов, В. (2019). Инфляцион таргетлаш сиёсатинининг хусусиятлари ҳамда уни ўзбекистонда жорий этиш истиқболлари. https://journal.tsue.uz/index.php/archive/article/view/1079