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Formalization, software implementation and accuracy testing of interval forecasting of real dynamic indicators with different statistical properties using logistic regression methods with and without regularization have been carried out in the paper. The interval forecasting of dynamic indicators involves determining that their future values belong to the preset intervals based on the probability estimates. Since we do not estimate a future value of the indicator, but rather the interval in which it is going to be located, we have called this forecasting method «interval forecasting». For accuracy testing of interval forecasting, we have used real dynamic indicators with different statistical properties which depend on the stationarity of location and scale parameters. For testing of stationarity of location parameter value with the passage of time, we have used a modified Kruskal-Wallis test, and for testing of stationarity of scale parameter value with the passage of time, we have used a modified Fligner-Killeen test. The results showed that in most cases for dynamic indicators with different statistical properties, the logistic regression model without regularization has demonstrated the best interval forecasting accuracy. Thus, in practice, we recommend the model of the logistic regression without regularization for the interval forecasting of real dynamic indicators, in particular, to construct forecasting ensembles.

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