Econometric Forecasting of Banking Indicators
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Beschrijving
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This monograph is devoted to the study of methods for forecasting economic indicators based on time-series analysis in the context of the increasing uncertainty and volatility of the modern economy. It examines the theoretical foundations of dynamic process modeling and provides a systematic overview of classical statistical approaches (AR, ARIMA, exponential smoothing models, etc.) and modern machine learning methods, including neural networks and gradient boosting algorithms. Particular attention is paid to the criteria for selecting an appropriate model for specific economic conditions, issues of data preprocessing, and the assessment of forecast accuracy and stability.
Beschrijving
(1)
This monograph is devoted to the study of methods for forecasting economic indicators based on time-series analysis in the context of the increasing uncertainty and volatility of the modern economy. It examines the theoretical foundations of dynamic process modeling and provides a systematic overview of classical statistical approaches (AR, ARIMA, exponential smoothing models, etc.) and modern machine learning methods, including neural networks and gradient boosting algorithms. Particular attention is paid to the criteria for selecting an appropriate model for specific economic conditions, issues of data preprocessing, and the assessment of forecast accuracy and stability.
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