In this paper, we consider characteristics of remote time period forecasting in the case when particular time periods could be described with innovation curves. Also, time series, whose periods determined by the curve types known, are not clearly seen. However, the fact that the time series describe projects with the same product evolution is known, and the time when new generations appear is determined.
Keywords
Time SeriesInnovation CurveForecastingProduction and Economic SystemFunction DescriptionDynamic Time Warping
References
Aufmann R.N., Barker V.C., Nation R., 2008. College algebra, Houghton Mifflin. Boston.
Wolberg J., 2006. Data analysis using the method of least squares extracting the most information from experiments, Springer. Berlin.
Amberg M., Mylnikov, L., 2009. Innovation project lifecycle prolongation method. Innovation and knowledge management in twin track economies: challenges & solutions № 1-3.
Knuth D. E., 1997. The art of computer programming. Reading, Mass. Addison-Wesley, 3rd ed-е.
Luzianin I., Krause B., 2016. Modeling of Selfsimilar Traffic. Proceedings of International Conference on Applied Innovation in IT, № 4.
Mylnikov L., 2015. Conceptual Foundations of Modelling of Innovative Production Projects. Proceedings of International Conference on Applied Innovation in IT, № 3.
Mylnikov, L., Amberg M., 2013. The Forecasting of Innovation Projects Parameters. 21st International-Business-Information-Management-Association Conference on Vision 2020: Innovation, Development Sustainability, and Economic Growth.
Crownover, Richard M., 1995. Introduction to fractals and chaos, Jones and Bartlett. Berlin.
Feder, Jens. Fractals., 1988. Physics of Solids and Liquids, Plenum Press. New York.
Bjorck A., 1996. Numerical Methods for Least Squares Problems, SIAM, Philadelphia.