The use of IT in the process of forming the concept of sustainable development of the region
Businesses that traditionally have a very large share of random factors can use models that allow to calculate only the probability that some future value will lie in a certain interval. The time interval for which there is a need to forecast ahead when solving a specific problem is called the lead period. In addition to calculating the best forecast, it is also necessary to specify its accuracy in order to assess the risk associated with decisions based on the forecast. Simple parametric trend models do not always provide an efficient calculation of the future behavior of objects. A definite alternative is iterative models based on the concept that time series with expressive autocorrelation should be considered as the result of some transformation of a sequence of independent pulses corresponding to "white noise". Autoregression and moving average models of a sufficiently high order can well approximate almost any stationary process. In this regard, the AP model is often used to model residuals in a particular parametric model, such as a regression model or a trend model. To achieve greater flexibility in adapting the model to the observed time series, it is often advisable to combine both transformations in one model, obtaining a combined autoregression - moving average (ARCS) model.