D.V. Yashin – Post-graduate Student, Ulyanovsk State Technical University

E-mail: dv.yashin@yandex.ru

E.N. Egov – Assistant, Ulyanovsk State Technical University E-mail: e.egov@ulstu.ru

In this article, we propose a method based on machine learning for choosing forecasting models in a combined model. We propose a neural network at the input of which the vector of time series metrics. Metrics correspond to significant characteristics of the time series. The values of the metrics are easily computed. The neural network calculates the estimated prediction error for each model from the base set of the combined model. The proposed selection method is most effective for short time series and when the base set contains a lot of complex prediction methods.

1. Computational Intelligence in Forecasting (CIF). URL = http://irafm.osu.cz/cif/main.php (ot 26.04.2018).
2. Yarushkina N.G., Filippov A.A., Romanov A.A., Moshkin V.S., Egov E.N. Developing a system for time series data mining on the basis of F-transform and the domain-specific ontology // Joint 17th world congress of international fuzzy systems association and 9th international conference on soft computing and intelligent systems (IFSA-SCIS). 2017. Institute of Electrical and Electronics Engineers Inc. 2071.
3. Afanas’ev V.N., Yuzbashev M.M. Analiz vremenny’x ryadov i prognozirovanie. M.: Finansy’ i statistika. 2001. 228 s.
4. Vasil’ev A.A. Genezis gibridny’x modelej prognozirovaniya na osnove ob’‘edineniya prognozov // Vestnik TvGU. Ser. E’konomika i upravlenie. 2014. № 23. S. 316−331.
5. Gorelik N.A., Frenkel’ A.A. Statisticheskie problemy’ e’konomicheskogo prognozirovaniya // Statisticheskie metody’ analiza e’konomicheskoj dinamiki: Uch. zap. po statistike. M.: Nauka. 1983. T. 46. S. 9−48.
6. Kobzar’ A.I. Prikladnaya matematicheskaya statistika. Dlya inzhenerov i nauchny’x rabotnikov. M.: Fizmatlit. 2006. 816 s.
7. Lukashin Yu.P. Adaptivny’e metody’ kratkosrochnogo prognozirovaniya vremenny’x ryadov. M.: Finansy’ i statistika. 2003. 416 s.
8. Sadovnikova N.A., Shmojlova R.A. Analiz VR i prognozirovanie . M.: MGUE’SI. 2001. 67 s.
9. Timina I.A., E’gov E.N. E’ntropiya kak pokazatel’ anomalij vremenny’x ryadov metrik proektnoj deyatel’nosti / Nechetkie sistemy’ i myagkie vy’chisleniya. Promy’shlenny’e primeneniya // Sb. nauchny’x trudov IV Vseros. nauchno-prakticheskoj mul’tikonferenczii s mezhdunar. uchastiem «Prikladny’e informaczionny’e sistemy’ (PIS). 2017. Ul’yanovsk: Ul’yanovskij gosudarstvenny’j texnicheskij universitet. 2017. S. 47−53.