FORECASTING DEVELOPMENT OF ECONOMIC PROCESSES USING ADAPTED NONLINEAR DYNAMICS METHODS

In the work, the authors propose using adapted nonlinear dynamics methods to prepare time series data for the forecast procedure in order to identify chaotic dynamics and to select forecast methods and models. Discussion: Each step of the proposed set of methods for data preprocessing allows us to put forward proposals on certain properties of the studied time series. This, in turn, proves that to obtain reliable and reasonable conclusions about the type of behavior of the investigated system, the results of one of the many existing tests are not enough. Results: Conducting a comprehensive analysis, will most correctly determine the type of behavior of the time series and its characteristics, which will make it possible to obtain a reliable forecast in the future. Key words: Non-linear trend, linear trend, visualization, Gilmore test, pseudophase space, attractor