STATISTICAL ANALYSIS OF TIME TRENDS IN FORECASTING ECONOMIC PROCESSES

The article is devoted to the study of statistical analysis of time trends as a key tool in forecasting economic processes. In the context of high dynamism in the modern economy and the limited predictability of market changes, the effective modeling of trends in economic indicators is of particular importance. The paper examines the sequence of actions in the process of statistical forecasting, starting from preliminary data processing to the selection of an appropriate time series model. The primary focus is on analyzing the trend component as a determining factor in the dynamics of economic indicators. It is shown that a trend is an analytical function that reflects the general tendency of an indicator’s change over time and serves as a basis for constructing forecasting models. The presence of a trend significantly affects the results of correlation analysis, as it may distort the real closeness of the relationship between variables by superimposing long-term tendencies on short-term fluctuations. The importance of detecting and correctly accounting for the trend in model construction is emphasized, particularly by either isolating or eliminating the trend component depending on the research goals. The method of time series extrapolation is considered one of the most accessible and widely used forecasting tools, relying on the assumption of consistent patterns in the development of an indicator over time. The quality of forecasts is shown to depend on the availability of sufficient statistical data and the justified choice of the type of smoothing line, which must align not only with mathematical parameters but also with the economic nature of the phenomenon. The article also explores the potential to enhance time series models by integrating spatial variables and hierarchical data levels, which is especially relevant for regional forecasting and the analysis of complex economic structures. Particular attention is paid to the issues of autocorrelation and multicollinearity that often arise in multidimensional models. The aim of the article is to substantiate methodological approaches to the statistical analysis of time trends in forecasting economic processes by identifying their impact on forecast accuracy, defining the conditions for effective application of extrapolation methods, and exploring the possibilities of integrating spatial and multifactor characteristics into the modeling of economic dynamics.