Mathematics methods, models and information technologies in economics

Fractal analysis and trends in innovative process at industrial enterprises

Economic Annals-XXI

Economic Annals-ХХI: Volume 143, Issue 7-8(2), Pages: 65-68

Citation information:
Chaikovska, I. (2014). Fractal analysis and trends in innovative process at industrial enterprises. Economic Annals-XXI, 7-8(2), 65-68. https://ea21journal.world/index.php/ea-v143-16/

View Full TextDownload PDF

Inna Chaikovska
PhD (Economics),
Khmelnytsky University of Management and Law
8 Theatralna Str., Khmelnytsky, 29000, Ukraine
inna.chaikovska@gmail.com

Fractal analysis and trends in innovative process at industrial enterprises

Abstract. The current stage of the world economy development is characterized by rapid growth in innovative component of industrial enterprises, which are the most active members of scientific-technological progress. Despite the fact that Ukraine has a very high intellectual potential, innovative part of economic development ensuring is used poorly. As innovative development is a prior economic strategy, a problem of its forecasting is quite relevant. For reliable forecast one has to examine time series of innovative processes and establish whether it is persistence (anti-persistence), when behavior is generated by a deterministic nonlinear law or it is completely random and to identify trends is the most appropriate method of forecasting. In the paper, we propose to implement innovative development of modeling based at fractal approach, which allows visualizing the mechanisms and predicting the direction of development. Fractal analysis is carried out using R/S method and Hurst exponent. The author analyzes the time series of innovations in industry on the example of Khmelnitsky region, namely: the proportion of firms that have introduced innovations, new processes, development of innovative products; the share of innovative products sales in the industry. We determine the classification of numerical series: random process, persistence property, property anti-persistence. The basic trends in the innovation processes are formulated by persistence (anti-persistence) determination and by prediction of innovative activity up-growth (using the method of exponential smoothing and moving average). The results have shown disappointing prospects for the future; hence, it requires activation of the innovative activity of industrial enterprises in Ukraine.

Keywords: Innovation; Fractal Properties; Hurst Exponent; R/S Analysis; Prediction

JEL Classification: C13; C53; C60

References

  1. Mandelbrot, B., & Hudson, R. (2004). The (Mis)Behavior of Markets: A Fractal View of Financial Turbulence. New York: Basic Books.
  2. Peters, E. (1991). Chaos and Order in the Capital Markets. New York: John Wiley.
  3. Afanasova, M. A. (2009). Managing knowledge-based integrated structures in innovation-active regions. Fundamentalnye issledovaniia (Fundamental Research), 3, 111-112 (in Russ.).
  4. Dubnytskyi, V. Yu. (2011). Choosing a method of predicting the value of securities based on the fractal dimension of a number of observations. Biznes Inform (Business Inform), 7(1), 120-121 (in Ukr.).
  5. Novikova, N. B. (2011). Fractal methods and the concept of the minimum cost of production systems in management of innovation. Vestnik YuRGTU (Herald of NPI), 2, 162-166 (in Russ.).
  6. Naiman, E. (2009). Calculation of the Hurst exponent to identify trend (persistence) of financial markets and macroeconomic indicators. Ekonomist (Economist), 10, 25-29 (in Russ.).
  7. Chaikovska, I. I. (2012). Complex management model of the enterprise intellectual capital. Ekonomicnij Casopis-XXI (Economic Annals-XXI), 7-8, 75-79.
    Retrieved from https://ea21journal.world/index.php/ea-v121-22/ (in Ukr.)
  8. Chaikovska, I. I. (2014). Economic-mathematical modeling in the management of the enterprise intellectual capital. Khmelnytsky: Khmelnytsky University of Management and Law (in Ukr.).
  9. Chaikovska, I. I. (2014). Usage of modern information technologies for economic processes modeling based at fractal analysis. Universitetski naukovi zapysky (University Scientific Notes), 1, 378-387 (in Ukr.).
  10. Chaikovska, I. I. (2014, January 30-31). Some aspects of fractal analysis application in the study of economic processes. Information society: technological, economic and technical aspects of formation (pp. 10-11). Ternopil: Taipei (in Ukr.).
  11. Lykov, I. A., & Okhotnikov, S. A. (2013). Effect of changes in the Hirst function on opportunities for economic growth forecasting. Fundamentalnye issledovaniia (Fundamental Research), 10, 1539-1544 (in Russ.).
  12. Clegg, R. G. (2005). A practical guide to measure the Hurst parameter (Computing science technical report). CS-TR-916, 125-138.
  13. Bistrai, G. P., Korshunov, L. A., Lykov, I. A. et al. (2010). Methods of nonlinear dynamics in the analysis and forecasting of economic systems at the regional level. Zhurnal ekonomicheskoi teorii (Journal of Economic Theory), 3, 203-114 (in Russ.).
  14. Bystrai, G. P., Korshunov, L. A., Nikulina, N. L. et al. (2010). Diagnosis and prognosis of the regions socio-economic development in the framework of nonlinear dynamics. Vetstnik Tiumenskoho hosudarstvennoho universiteta (Herald of the Tiumen State University), 4, 164-170 (in Russ.).
  15. Department of the State Service of Statistics in Khmelnytsky region (Official website).
    Retrieved from http://www.km.ukrstat.gov.ua/ukr/index.htm

Received 10.06.2014

View Full TextDownload PDF
In, , , , ,

More Papers