INFLYATSIYA BOZORLARIDA GIBRID ECONOMETRIK VA MASHINA O'RGANISH YONDASHUVLARI ORQALI INFLYATSIYA BOSHQARISHINI YAXSHILASHTIRISH
Kalit so'zlar
https://doi.org/10.47390/ydif-y2025v1i9/n06Kalit so'zlar
Inflyatsiya prognozi, Ekonometrika, Mashinani o'rganish, Gibrid modellar, Rivojlanayotgan bozorlar, Pul-kredit siyosati, Bashoratli aniqlik.Annotasiya
Ushbu tadqiqot ekonometrik va mashina o'rganish yondashuvlarini birlashtirgan gibrid tizim orqali rivojlanayotgan bozorlarda inflyatsiya prognozining aniqligini oshirishni o'rganadi. ARIMA va VAR kabi an'anaviy modellar nazariy talqin qilishni ta'minlaydi, ammo o'zgaruvchan iqtisodiyotlarga xos bo'lgan chiziqli bo'lmagan dinamika va tarkibiy o'zgarishlarni aniqlashda qiynaladi. Bunga yechim topish uchun tadqiqot ekonometrik texnikalarni Random Forest, Gradient Boosting va LSTM tarmoqlari kabi mashina o'rganish algoritmlari bilan birlashtiradi. 2000-yildan 2024-yilgacha bo'lgan oylik makroiqtisodiy ma'lumotlardan foydalangan holda, ARIMA-LSTM va VAR-Random Forest gibrid modellari an'anaviy usullarga qaraganda 15-25% kamroq prognozlash xatolariga erishdi. Natijalar shuni ko'rsatadiki, gibrid modellar talqin qilish va bashorat qilish aniqligini samarali ravishda muvozanatlashtiradi, siyosatchilarga o'z vaqtida va ma'lumotlarga asoslangan pul-kredit siyosatini ishlab chiqish uchun amaliy vositalarni taklif qiladi. Tadqiqotda rivojlanayotgan iqtisodiyotlarda bunday modellarni samarali qo'llash uchun ma'lumotlar sifati, institutsional salohiyat va fanlararo ko'nikmalarning muhimligi ta'kidlangan.
Manbalar
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