Models and procedures for classification and forecasting of nondeterministic processes according to chaotic dynamics parameters
Keywords:antipersistent hour series, time series classification, modeling, interpolation, short string prediction, accuracy of classification algorithms, software security
The article investigates the processes of classification, modeling and short-term prediction of nondeterministic time sequences, which are represented by antipersistent time series (ATS). The subject of analysis - procedures for classification and forecasting the pa-rameters of such models. The object of research is the processes of modeling and analysis of parameters of nondeterministic time series of ATS with a uniform step. The aim of the work is to increase the efficiency and accuracy of methods and algorithms for classification, modeling and forecasting of ATS. Models and methods of fractal analysis are used to study the properties of ATS, on the basis of which the categories of processes of numerical series are established. With the help of aggregation of ATS levels correct mathematical models of classification of nondeterministic time sequences are developed, and also algorithmic and software means of their realization are formed. Examples of models of numerical series obtained using the aggregation procedure presented in the study are given. It is established that the most detailed and stable is the classification of ATS based on data aggregation schemes without level crossing. The comparative analysis of numerical efficiency of algorithms of classification of ATS is carried out and the task of formation of procedures of interpolation and short-term forecasting of ATS is realized. An instrumental software environment is presented, which provides a correct study of algorithms for modeling and classification of antipersistent time series. Recommendations on the procedures for modeling ATS classification algorithms are of practical importance.
Zhou, Renjie & Yang, Chen & Wan, Jian & Zhang, Wei & Guan, Bo & Xiong, Naixue. (2017). Measuring Complexity and Predictability of Time Series with Flexible Multiscale Entropy for Sensor Networks. Sensors. 17. 787. 10.3390/s17040787.
Liu, Xiaofeng & Jiang, Aimin & xu, Ning & Xue, Jianru. (2015). Increment Entropy as a Measure of Complexity for Time Series. Entropy. 18. 10.3390/e18010022.
Nazzal, Mohammad & Saad, Mohammed. (2018). A general framework for sustainability assessment of manufacturing processes. Ecological Indicators. 97. 10.1016/j.ecolind.2018.09.062.
Moulder, Robert & Daniel, Katharine & Teachman, Bethany & Boker, Steven. (2019). Tangle: A Metric for Quantifying Complexity and Erratic Behavior in Short Time Series. 10.31234/osf.io/23csa.
Montesinos, Luis & Castaldo, Rossana & Pecchia, Leandro. (2018). On the use of approximate entropy and sample entropy with centre of pressure time-series. Journal of NeuroEngineering and Rehabilitation. 15. 116. 10.1186/s12984-018-0465-9.
Epskamp, Sacha & Waldorp, Lourens & Mõttus, René & Borsboom, Denny. (2018). The Gaussian Graphical Model in Cross-Sectional and Time-Series Data. Multivariate Behavioral Research. 53. 1-28. 10.1080/00273171.2018.1454823.
Skalozub V.V., & Klymenko Y.V. (2016). The development of analysis and fore-casting procedures of non-deterministic technological and economic processes on the basis of indicators of chaotic dynamics. Economics: Time Realities, 4(26), 82-90.
Skalozub, V. V., & Klymenko, Y. V. (2018) Method for planning non-determined operation processes of railway technical system park. Nauka ta prohres transport. Visnyk Dnipropetrovskoho natsionalnoho universytetu zaliznychnoho transportu imeni akademika V. Lazariana, 5(77), 7-18.
Skalozub, V. V., Zhukovitskiy, I. V., Klimenko, I. V., & Zaets, А. Р. (2018). Creation of Intellectual Decision Support Systems in a Unified Automated System for Managing Rail Freight in Ukraine. Systemni tekhnolohii: Rehionalnyi mizhvuzivskyi, 3(116), 153-162.
Skalozub V.V. & Klymenko I.V. (2016) Estimation and Prediction of the Parameters of Time Series of Traffic Volumes Based on the Specialized Procedures of the Fractal Analysis. Naukovyi visnyk Khersonskoho derzhavnoho universytetu, 20 (II), 189-192.
Skrynyk, Oleg & Skynyk, Olesya & Oshurok, Dmytro. (2013). Fractal analysis of air temperature time series collected in Ukraine. Hidrolohiia, hidrokhimiia i hidroekolohiia. 2(29). 89-95.
Boyko, R. О., Zagorovska, L. G., & Yarova, Т. V. (2013) Informative Providing of Task of Prognostication of Feasibility Indicators of Functioning of Technological Complex. Eastern-European Journal of Enterprise Technologies, 3(10), 62-64.
Dolgіh, A.O. and Bajbuz, O.G. (2019) Comparative analysis of time series forecasting models effectiveness by means of a multi-criteria procedure on the example of financial indicators. “The Journal of Zhytomyr State Technological University”? Section 1: Engineering, 1(83), 130-141.
Korablev, N.M, and Ivashchenko G.S. (2015) Application of the models of artificial immune systems for short-term forecasting of time series containing abnormal values. Bionics of Intelligense: Sci. Mag, 2 (85), 95-99.
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