Intellectual analysis of time series of agricultural production indicators

In the conditions of the market and the transition of the agricultural industry to an innovative path of development, the uncertainty of factors that determine its dynamics increases significantly. Forecasting changes is the most important function of management. Dynamic processes occurring in agricultural production are presented as usually in time series. This actualizes the problem of increasing the validity and accuracy of forecasts based on time series analysis in conditions of great uncertainty. The models and methods that are currently used to predict the dynamics of agricultural processes are carried out within the framework of a statistical approach, the application of which is based on a number of requirements. However, for time series reflecting real dynamic processes occurring in agricultural production, these requirements are rarely met due to the presence of non-statistical uncertainty. At present, intelligent methods for predicting the dynamics of processes are actively developing, which are based on a fuzzy time series model. The problem of forecasting just such series is of particular relevance for agricultural science and practice. Fuzzy methods for forecasting time series include methods such as: methods of fuzzy regression analysis; methods of fuzzy autoregressive analysis; methods of fuzzy neural network analysis; methods of analysis of fuzzy trends. This article discusses the possibilities of using fuzzy autoregressive analysis tools to predict the dynamics of processes in the agricultural sector of the economy. At present, intelligent methods for predicting the dynamics of processes are actively developing, which are based on a fuzzy time series model. The problem of forecasting just such series is of particular relevance for agricultural science and practice

Keywords: time series, forecast, fuzzy modeling, agricultural production

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