GDP FORECASTING USING THE LINEAR CELLULAR AUTOMATON METHOD

This study examines the application of the linear cellular automaton method for forecasting the dynamics of gross domestic product (GDP) based on multidimensional economic time series. The methodological framework includes an analysis of the algorithmic properties of the linear cellular automaton, aimed at identifying patterns of macroeconomic indicator changes under conditions of high uncertainty and economic instability. An empirical analysis of the forecasting accuracy of the method was conducted using Russian GDP data for the period 2000–2024, confirming the model’s high adaptability and robustness to economic cycle fluctuations. The study discusses issues related to preprocessing of raw data, formalization of criteria for evaluating forecasting success, as well as the limitations and potential for further development of the method within the context of econometric modeling and applied economic analysis.