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Identification anomalies the time series of metrics of project based on entropy measures

Keywords:

I.A. Timina – Assistant, Department «Information Systems», Ulyanovsk State Technical University
E-mail: i.timina@ulstu.ru
E.N. Egov – Assistant, Department «Information Systems», Ulyanovsk State Technical University
E-mail: kater73ru@rambler.ru
Yu.P. Egorov – Dr. Sc. (Eng.), Professor, Main Research Scientist, FRPC OJSC «RPA «Mars» (Ulyanovsk)
E-mail: yupe@mail.ru
D.V. Yashin – Assistant, Department «Information Systems», Ulyanovsk State Technical University
E-mail: dv.yashin@ulstu.ru
S.K. Kiselev – Dr. Sc. (Eng.), Professor, Head of Department «Measuring and Computing Systems», Ulyanovsk State Technical University
E-mail: ksk@ulstu.ru


In many areas of the modern world, forecasting is the key to successful tasks. This can be the forecasting of financial market behavior or the forecasting of an offensive situation of a technical product. There are many prediction methods and new ones are being developed. Each of them will give a better forecast for some types of time series and yield to other methods for other types of time series. Therefore, the primary task is to find the optimal method or forecast methods that will give the best forecast. To this end, an aggregator is developed that will automatically determine the methods that are best suited for analyzing and forecasting time series.
Also in this paper we present two additional solutions to the problem of predicting a new value for a time series by choosing the best hypothesis and using the measures of entropy of fuzzy time series (TS).
The first method is based on the identification of three hypotheses of prediction. With the possibility of adjusting the received forecast if there is a predictor time series.
The second method applies statistical analysis in analyzing the behavior of BP during the transition from one point to another. The behavior analysis is carried out on the basis of a pair of parameters.

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