Forecasting Methodology of Scientific Investigations and Innovations Sphere’s Indicators by Means of Neural Networks

The aim of the work is to elaborate the methodology, economic and mathematic models and tool means for short-term forecasting the scientific investigations and innovations sphere’s indicators. It considers the system of neural networks forecasting the economic indicators as a part of hybrid, regressive and intellectual forecasting system, and its implementation as exemplified by the scientific investigations and innovations sphere’s indicators in the Russian Federation. The forecasting data by 72 measures (96%) out of 75 scientific investigations and innovations sphere’s indicators were successfully modeled and received. The conclusion is made on the basis of computer experiment that the use of such a system allows not only to raise the accuracy and quality of the forecast calculations, but to use them also in management outlines for achieving the target indicators. The further investigations are aimed at system parameters’ optimization in order to improve the efficiency without losing accuracy and quality, improving the management service of indicators’ models calculations.

innovations, investigations and innovations sphere, the systems of regressive equations, short-term forecasting models, forecasting retro verification, the system of neural networks forecasting models, the system of hybrid models

1. Rossiiskii statisticheskii ezhegodnik. 2015: stat. sb. [Russian Statistical Yearbook. 2015. Statistical handbook]. M.: Rosstat—Rosstat, 2015, 728 p. (in Russian).

2. Kolmakov I.B., Kol’tsov A.V., Domozhakov M.V. Osnovy postroeniia sistemy kompleksnogo prognoza sfery issledovanii i innovatsii vo vzaimosviazi s makroekonometricheskimi modeliami ekonomiki Rossii [Fundamentals of integrated forecast system research and innovation in relation to the macroeconometric model of the Russian economy]. Innovatika i ekspertiza—Innovation and Expertise, 2015, № 1 (14), pp. 255–275 (in Russian).

3. Dougerti K. Vvedenie v ekonometriku [Introduction to econometrics]. Moscow, Infa-M — Infa-M, 1999, 402 p. (in Russian).

4. Kitova O.V., Kolmakov I.B., Kol’tsov A.V., Domozhakov M.V.Analiz dinamiki rezul’tatov verifikatsii kratkosrochnykh prognozov pokazatelei sfery nauchnykh issledovanii i innovatsii v Rossii [The analysis of the dynamic verification results of the short-term predictions for the research and innovation indicators in Russia]. Vestnik Rossiiskogo ekonomicheskogo universiteta im. G.V. Plekhanova—Vestnik of the Plekhanov Russian University of Economics, 2016, no. 5, pp. 160–172 (in Russian).

5. Kolmakov I.B., Domozhakov M.V. Sintez ekonometricheskikh i neirosetevykh sistem prognoza pokazatelei sfery issledovanii i innovatsii v Rossiiskoi Federatsii [Synthesis of econometric models and neural network forecasting indicators in research and innovation in the Russian Federation]. Upravlencheskie nauki — Management Sciences, 2016, no. 2, pp. 27–37 (in Russian).

6. Kitova O.V., Kolmakov I.B., SharafutdinovaA.R.Analiz tochnosti i kachestva kratkosrochnogo prognoza pokazatelei sotsial’no-ekonomicheskogo razvitiia Rossii [Analysis of the accuracy and quality of short-term forecast of social and economic development of Russia]. Vestnik Rossiiskogo ekonomicheskogo universiteta im. G.V. Plekhanova—Vestnik of the Plekhanov Russian University of Economics, 2013, no. 9, pp. 111–119 (in Russian).

7. Khaikin S. Neironnye seti: polnyi kurs. 2-e izd [Neural Networks: A Comprehensive Foundation. 2th ed.]. Moscow, Vil’iams—Williams, 2006, 1104 p. (in Russian).

8. Strugailo V.V.Ispol’zovanie kombinirovannoi struktury iskusstvennoi neironnoi seti dlia raspoznavaniia obrazov [[Using a combined structure of an artificial neural network pattern recognition]. Nauka i obrazovanie: nauchnoe izdanie MGTU im. N.E. Baumana — Science and education: scientific publication MSTU Bauman, 2012, no. 3, p. 24 (in Russian).

9. Kognitivnaia biznes-analitika: uchebnik / pod red. N.M.Abdikeeva [Cognitive business intelligence: Textbook. Under the editorship of N.M.Abdikeev]. Moscow, Infra-M—Infra-M, 2011, 511 p. (in Russian).

10. Amosov O.S., Pashchenko F.F., Muller N.V. Strukturno parametricheskaia identifikatsiia vremennogo riada s primeneniem fraktal’nogo i veivlet-analiza [[Structural parametric identification of time series with frontal and wavelet analysis]. Informatika i sistemy upravleniia — Information and Managment Systems, 2015, no. 2 (44), pp. 80–88 (in Russian).

11. IvanjukV.A., Pashhenko F.F. Methods and models for the forecasting and management of time series / Proceedings of International work-conference on Time Series (ITISE2015, Granada, Spain). Granada, 2015, pp. 283–292.