I.A. Timina - Assistant, Department «Information Systems», Ulyanovsk State Technical University. E-mail: i.timina@ulstu.ru Yu.A Radionova - Ph. D. (Eng.), Leading Part-programming engineer, FRPC OJSC «RPA «Mars» (Ulyanovsk). E-mail: julia-owl@mail.ru N.G. Yarushkina - Dr. Sc. (Eng.), Professor, Head of Department «Information Systems», Ulyanovsk State Tech-nical University. E-mail: jng@ulstu.ru E.N. Egov - Assistant, Department «Information Systems», Ulyanovsk State Technical University. E-mail: kater73ru@rambler.ru

The activities of any research enterprise lies in the design, development and support of scientific and technical products. For making var-ious kinds of solutions the company is based on modern tools and methods of information processing within the corporate information system of the enterprise (CISE). One of the Central elements in the construction of effective CISE is to automate the activities of the de-sign team, whose main goal is through the analysis of incoming technical documentation of the enterprise to make quick and correct con-clusions on the execution and implementation of ongoing projects. In this work the problem of data mining by algorithmic prediction through the use of fuzzy tendencies (FT) and measures of entropy of fuzzy time series (TS), is formed on the basis of technical documentation for products in the archive project organization. Among the obtained ranks FT using the degree of similarity pairs are formed similar TS FT. Based on the data organization in pairs is al-located a predicate that has a direct impact on the prognosis dependent BP. Impact VR-predicate is expressed by regulating predict de-pendent BP. The application of the results of measures of entropy fuzzy TS and FT allows you to select a hypothesis for forecasting. Thus, it can be used to determine whether the hypothesis of conservation trends when forecasting or should we expect the trend change.

 

1. Ruey-Chyn Tsaur, Jia-Chi O Yang, Hsiao-Fan Wang. Fuzzy Relation Analysis in Fuzzy Time Series Model // Computers and Mathematics with Applications. 2005. 49.
2. Namestnikov A.M., Yarushkina N.N. Efficiency of Genetic algorithms for automated design problems // IzvestijaRossijjskojjakademiinauk. Teorija i sistemy upravlenija. 2002. № 2. S. 127−133.
3. Yarushkina N.G. Soft Computing and complex system analysis // Internatiomnal Jornal of General Systems. 2001. T. 30. № 1. S. 71−88.
4. Boks Dzh., Dzhenkins G. Analiz vremennykh rjadov. Prognoz i upravlenie / Per. s angl. / Pod red. V.F. Pisarenko. M.: Mir. 1974. 406 s.
5. Kendehl M. Vremennye rjady / Per. s angl. i pred. JU.P. Lukashina. M.: Finansy i statistika. 1981. 199 s.
6. Lotfi A. Zadeh. Fuzzy Sets // Information and Control. 1965.
7. Song Q., Chissom B. Fuzzy time series and its models // Fuzzy Sets and Systems. 1993. № 54 P. 269−277.
8. JArushkina N.G., Afanaseva T.V., Perfileva I.G. Intellektualnyjj analiz vremennykh rjadov: ucheb. posobie. Uljanovsk: UlGTU. 2010. 320 s.
9. JArushkina N.G., Perfileva I.G., Afanaseva T.V. Integracija nechetkikh modelejj dlja analiza vremennykh rjadov // Izvestija Samarskogo nauchnogo centra Rossijjskojj akademii nauk. 2010. T. 12. № 4−2.S. 506−509.
10. JArushkina N.G., Perfileva I.G., Afanaseva T.V. Integralnyjj metod nechetkogo modelirovanija i analiza nechetkikh tendencijj // Avtomatizacija processov upravlenija. 2010. № 2. S. 59−63.
11. JArushkina N.G., Afanaseva T.V., Romanov A.A., Timina I.A.Izvlechenie znanijj o zavisimostjakh vremennykh rjadov dlja zadach prognozirovanija // Radiotekhnika. 2014. № 7. S. 141−146.
12. Timina I.A. Nechetkaja zavisimost kak metod reshenija zadach intellektualnogo analiza vremennykh rjadov // Avtomatizacija processov upravlenija. Uljanovsk: «NPO «Mars». 2013. № 3(33). S. 39−44.
13. Yarushkina N., Afanasieva T., Timina I. Predicative analytics for developing software // VIII Spring / Summer Young Researchers - Colloquium on Software Engineering (SYRCoSE-2014). May 29−31. SaintPeterburg, Russia. P. 154−158.