COMPARATIVE ANALYSIS OF ARIMA AND LSTM MODELS IN FORECASTING THE CONSUMER PRICE INDEX UNDER CONDITIONS OF HIGH INFLATIONARY VOLATILITY

Abstract

This study focuses on improving forecasting models for Ukraine's macroeconomic indicators under conditions of volatility and exogenous shocks. The scientific novelty lies in the integration of neural network theory with the principles of dynamic system identification. The LSTM architecture effectively models significant time lags and resolves the vanishing gradient problem. Model parameters are determined using iterative optimization algorithms to minimize the loss function. Computational experiments on statistical data confirm the superiority of deep learning methods over linear approaches. The results justify the use of non-linear models to account for economic inertia and enhance the accuracy of long-term forecasting