RAQAMLI TEXNOLOGIYALARNING YANGI O‘ZBEKISTON RIVOJIGA TA’SIRI Xalqaro ilmiy-amaliy konferensiyasi that ARIMA assumes linearity, stationarity, and absence of outliers, and the model's performance
depends on the quality and characteristics of the data being analyzed.
Abbreviations AI –
Artificial Intelligence
ARIMA – Auto Regressive Integrated Moving Average
DS –
Data Science
GDP –
Gross Domestic Product
ML –
Machine Learning
OLS – Ordinary Least Squares
RFR - Random Forest Regression
REFERENCES: 1.
Adam Richardson, T. v. (2018 ). Nowcasting New Zealand GDP using machine learning
algorithms. Big Data for Central Bank Policies / Building Pathways for Policy Making with Big Data.
Bali, Indonesia: Irving Fisher Committee on Central Bank Statistics .
2.
Dahai Wang, B. L. (2022). Emotion Recognition Algorithm Application Financial
Development and Economic Growth Status and Development Trend. Frontiers in Psychology.
3.
Dwarakanath G V, S. T. (2022). WORLD’S GDP PREDICTION USING MACHINE LEARNING.
Journal of Positive School Psychology, 1039-1047.
4.
Giovanni Maccarrone, G. M. (2021). GDP Forecasting: Machine Learning, Linear or
Autoregression? Frontiers in Artificial Intellegence.
5.
Li, Z. (2022). Neural network economic forecast method based on genetic algorithm. The
Institution Engineering and Technology .
6.
Oscar CLAVERIA, E. M. (2021). A GENETIC PROGRAMMING APPROACH FOR ESTIMATING
ECONOMIC SENTIMENT IN THE BALTIC COUNTRIES AND THE EUROPEAN UNION. Technological
and Economic Development of Economy, 262
–
279.
7.
Tanvi Gharte, H. P. (2022 ). GDP Prediction and Forecasting using Machine Learning .
International Research Journal of Engineering and Technology, 2908-2912.
