DOI
10.34229/KCA2522-9664.24.2.6
UDC 519.21
1 V.M. Glushkov Institute of Cybernetics, National Academy of Sciences of Ukraine, Kyiv, Ukraine
 knopov1@yahoo.com
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2 Prydniprovska State Academy of Civil Engineering and Architecture, Dnipro, Ukraine
a.s.korkhin@gmail.com
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DETERMINING A PIECEWISE-LINEAR TREND OF A NON-STATIONARY
TIME SERIES BASED ON INTELLIGENT DATA ANALYSIS.
II. MACHINE EXPERIMENTS AND PRACTICAL PROBLEM SOLUTIONS
Abstract. The article describes the results of the approbation of the method of constructing a piecewise-linear trend, which can have breaks at the switching points as well as be continuous at these points, i.e., represent a linear flow. An example of applying the method for constructing a linear regression with switches, which has two independent variables with a trend, is considered. The problem of spline approximation of the time series of logarithms of the number of infected people with COVID-19 in Ukraine is stated and solved.
Keywords: trend, regression, switch point, spline, real-time calculation.
full text
REFERENCES
- Knopov P.S., Korkhin A.S. Regression analysis under a priori parameter restrictions. Springer Optimization and Its Applications. Vol 54, New York, NY: Springer, 2011. https://doi.org/10.1007/978-1-4614-0574-0.
- Korkhin A.S. Parameter estimation accuracy for nonlinear regression with nonlinear constraints. Cybernetics and Systems Analysis. 1998. Vol. 34, N 5. P. 663–672. https://doi.org/10.1007/BF02667038.
- Knopov P.S., Korkhin A.S. Determination of a piecewise linear trend of a non-stationary time series based on intelligent data analysis. I. Description and justification of the method. Kibernetyka ta systemnyi analiz. 2024. Vol. 60, N 1. P. 61–72.
- Perron P., Zorta E. Estimation and inference of linear trend slope ratios with an application to global temperature data. Journal of Time Series Analysis. 2017. Vol. 38, N 5. P. 630–667.
- Korkhin A.S. Constructing a switching regression with unknown switching points. Cybernetics and Systems Analysis. 2018. Vol. 54, N 3. P. 443–455. https://doi.org/10.1007/s10559-018-0045-9.
- Korkhin A.S. An approximate method of constructing a switching regression with unknown switch points. Cybernetics and Systems Analysis. 2020. Vol. 56, N 3. P. 426–438. https://doi.org/10.1007/s10559-020-00258-1.
- Knopov P.S., Korkhin A.S. Statistical analysis of the dynamics of coronavirus cases using stepwise switching regression. Cybernetics and Systems Analysis. 2020. Vol. 56, N 6. P. 943–952. https://doi.org/10.1007/s10559-020-00314-w .
- Knopov P.S., Korkhin A.S. Dynamic models of epidemiology in discrete time taking into account processes with lag. Int. J. Dynam. Control. 2023. Vol. 11. P. 2193–2214. https://doi.org/10.1007/s40435-023-01135-3.
- Golodnikov A.N., Knopov P.S., Pepelyaev V.A. Estimation of reliability parameters under incomplete primary information. Theory and Decision. 2004. Vol. 57, N 4. P. 331–344. https://doi.org/10.1007/s11238-005-3217-9.
- Norkin V.I., Gaivoronski A.A., Zaslavsky V.A., Knopov P.S. Models of the optimal resource allocation for the critical infrastructure protection. Cybernetics and Systems Analysis. 2018. Vol. 54, N 5. P. 696–706. https://doi.org/10.1007/s10559-018-0071-7.
