Attractors in the Development Problem of the Forecasting Model on the Base of the Strictly Binary Trees

In this paper the questions of the choice of the training data sequence for the forecasting model on the base of the strictly binary trees which is usually applied to time series with a short length of actual part (about 15 30 elements) have been considered. It is offered to use the principles of the attractors forming in the presence of the long time series that will allow creating the training data sequence more reasonably. The reviewed examples confirm the efficiency of the attractors use in sense of minimization of the affinity indicator of the forecasting model, and also the forecasting errors on 1 5 steps forward. Besides, the minimization of time expenditures on development of the forecasting model is provided.

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