Predictive scenario modeling for the development of a regional innovation system based on data mining methods

The relevance of the study is determined by the need to improve the management efficiency of regional innovation systems (RIS) in the context of the digital transformation of the economy and limited budgetary resources. The Samara region, having significant scientific and technical potential, demonstrates insufficient dynamics of innovative development, which requires the development of scientifically sound forecasting tools. The academic literature offers numerous attempts to describe how innovation evolves within territories, and forecasting is generally viewed as a necessary analytical instrument. However, methodological support remains limited: few models can adjust to changing regional conditions while simultaneously reflecting heterogeneous determinants, feedback effects, and nonlinear links among participants of innovation activity. The article provides a justification and testing of methodological approaches to predictive and scenario modeling of RIS (using the Samara Region as an example) using multiple regression tools and a scenario‑based approach. Multiple linear regression, correlation analysis, step-by-step variable inclusion, and scenario forecasting were used to build the model. The verification of the obtained results was carried out on the basis of a comparative analysis with gradient boosting (XGBoost). The constructed regression model (R² = 0,94) includes seven predictors. The key factors are the number of small innovative enterprises (β = 0,350) and inventive activity (β = 0,240). Four scenarios have been developed for 2026–2029: basic (20,4% of GRP innovations), optimistic (24,8%), innovative (29,5%) and pessimistic (9,2%). The model is suitable for strategic planning of innovative development of the region. The key driver of growth is the support of small innovative businesses and the promotion of patent activity. The model is adaptable to other industrialized regions.

1. Plekhova Yu.O., Perova V.I. An innovative method for analyzing the management of socio-economic development of Russian regions using neural network modeling // Issues of Innovative Economy. 2025. Vol. 15, No. 1. Pp. 125–144. doi:10.18334/vinec.15.1.122530.

2. Kryzhko D.A. Conceptual modeling of the interrelationships between the development of innovative potential and the state of the regional innovation system // Bulletin of the Academy of Knowledge. 2024. No. 6 (65). URL: https://cyberleninka.ru/article/n/kontseptualnoe-modelirovanie-vzaimosvyazey-razvitiya-innovatsionnogo-potentsiala-i-sostoyaniya-regionalnoy-innovatsionnoy-sistemy (date of access: 28.06.2026).

3. Egorov N.E., Vasilyeva N.V. Assessment of the level of innovative development of regions based on the Triple Helix econometric model and the Russian regional innovation Index // Issues of Innovative Economics. 2022. No. 3. Pp. 1697–1710. doi:10.18334/vinec.12.3.115181.

4. Vasiliev V.L. Assessment of the effectiveness and balance of regional innovation systems based on the analysis of the development of the innovation process // Issues of Innovative Economy. 2026. Vol. 16, No. 1. Pp. 135–158. doi:10.18334/vinec.16.1.124755.

5. Pechatkin V.V. Assessment of the level of innovative development of Russian regions from the perspective of the effectiveness of innovation policy implementation // Issues of Innovative Economy. 2024. No. 4. Pp. 1191–1214. doi:10.18334/vinec.14.4.121690.

6. Planning the innovative development of regional systems based on the digitalization of state strategic management / E.V. Drobot, I.N. Makarov, T.V. Bashlykov [et al.] // Issues of Innovative Economy. 2024. No. 1. Pp. 139–156. doi:10.18334/vinec.14.1.120684.

7. Indicators of innovation activity, 2026 : statistical collection / V.V. Vlasova, L.M. Gokhberg, G.A. Gracheva [et al.] ; National Research University "Higher School of Economics". Moscow : HSE ISSEK, 2026. 216 p.

8. Grachev S.A., Vakulenko O.S. Assessment of the features of innovative development at the regional level // Economics, Entrepreneurship and Law. 2024. No. 12. Pp. 7177–7190. doi:10.18334/epp.14.12.122167.

9. Stroev V.V., Svistunov V.M. The effectiveness of implementing artificial intelligence for the development of Russian regions // Bulletin of the Altai Academy of Economics and Law. 2024. No. 7-1. Pp. 146–156. URL: https://vaael.ru/ru/article/view?id=3575 (date of access: 22.06.2026).

10. Mishchenko I.G., Golovacheva A.S. Modern trends and mechanisms of innovative development of regions: conceptual approaches, international practices and growth prospects // Beneficium. 2025. No. 3 (56). URL: https://cyberleninka.ru/article/n/sovremennye-tendentsii-i-mehanizmy-innovatsionnogo-razvitiya-regionov-kontseptualnye-podhody-mezhdunarodnye-praktiki-i-perspektivy (date of access: 28.06.2026).

11. Yanchenko E.V. Regional innovation ecosystem: assessment of the effectiveness of functioning in the context of digitalization // Issues of Innovative Economy. 2023. Vol. 13, No. 2. Pp. 881–900. doi:10.18334/vinec.13.2.117913.

12. Borisov S.A., Somenkova N.S. Assessment of the level of innovative development of a region in the context of economic security based on neural network modeling // Economic security. 2026. Vol. 9, No. 7. doi:10.18334/ecsec.9.7.126167.

13. Frolov V.G., Perova V.I. Analysis of the innovation-investment balance of Russia’s industrial policy in the context of digital transformation using artificial intelligence methods // Issues of Innovative Economy. 2023. No. 1. Pp. 127–148. – doi:10.18334/vinec.13.1.117247.

14. Innovative ecosystem for the development of regional economy / M.K. Ashinova, T.A. Paladova, E.S. Maltseva, A.A. Gorb // Issues of Innovative Economy. 2024. Vol. 14, No. 3. Pp. 833–846. doi:10.18334/vinec.14.3.121277.

15. Forecasting indicators of the region’s scientific, technological and innovative development using recurrent neural networks / V.I. Byvshev, S.A. Koroleva, I.A. Panteleeva, I.V. Pisarev // Economic and Social Changes: Facts, Trends, Forecast. 2024. No. 3. URL: https://cyberleninka.ru/article/n/prognozirovanie-pokazateley-nauchno-tehnologicheskogo-i-innovatsionnogo-razvitiya-regiona-pri-pomoshchi-rekurrentnyh-neyronnyh-setey (date of access: 28.06.2026).