Abstract
The task of designing information technology for time series forecasting, that bases on fuzzy expert evaluations was considered. A forecasting model, part of which is an expert’s unit, were proposed. The algorithm of synthesis predictive scheme based on the basic predictive models was developed. To determine expert evaluation of the forecast value, the task of forecasting was seen as the problem of numerical evaluation of object. The rules for determining the collective numerical evaluations, that are based on fuzzy expert assessments were developed. The developed rules take into account coefficients of experts’ competence and also their degree of confidence for their own assessments. The approaches to determining the competence coefficients members of the expert group were systematized. The analysis of features for designing information-analytical system of time series forecasting were done. The structural diagram of the analytical block of information-analytical system for time series prediction, that based on the fuzzy expert estimates, was itemized. The designed information technology should be used for time series forecasting in cases where it is necessary to take account the impact, on the process that is studied, of temporary, informal factors.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Tsmots, I.: Information Technology and Specialized Tools for Signal Processing and Image Processing in Real Time. UAD, Lviv (2005)
Mulesa, O., Geche, F., Batyuk, A.: Information technology for determining structure of social group based on fuzzy c-means. In: Xth International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT), pp. 60–62 (2015)
Kuharev, V.N., Sally, V.N., Erpert, A.M.: Economic-Mathematical Methods and Models in the Planning and Management. Vishcha School, Kiev (1991)
Kozadaev, A.S., Arzamasians, A.A.: Prediction of time series with the apparatus of artificial neural networks. The short-term forecast of air temperature. Bull. Univ. Tambov Ser. Nat. Tech. Sci. 11(3), 299–304 (2006)
Snytiuk, V.Y.: Forecasting. Models. Methods. Algorithms: Tutorial. “Maklaut”, Kiev (2008)
Mendel, A.S.: Method counterparts in predicting short time series: expert-statistical approach. Machine. Telemekh. № 4, pp. 143–152 (2004)
Mulesa, O., Geche, F.: Designing fuzzy expert methods of numeric evaluation of an object for the problems of forecasting. Eastern Eur. J. Enterp. Technol. 3(4(81)), 37–43 (2016). https://doi.org/10.15587/1729-4061.2016.70515
Mulesa, O.: Heuristic rules of the collective numerical evaluation of object and their application to the problem of time series prediction. In: Intelligent Decision Support Systems and Computational Intelligence problems, pp. 208–210 (2016)
Kuchanky, A., Biloshchytskyi, A.: Selective pattern matching method for time-series forecasting. Eastern Eur. J. Enterp. Technol. 6(4–78), 13–18 (2015). https://doi.org/10.15587/1729-4061.2015.54812
Zaichenko, Y.P., Mohammed, M., Shapovalenko, N.V.: Fuzzy neural networks and genetic algorithms in problems of macroeconomic forecasting. Scientific news “KPI” № 4, pp. 20–30 (2002)
Pukach, A., Teslyuk, V., Tkachenko, R., Ivantsiv, R.A.: Implementation of neural networks for fuzzy and semistructured data. In: 11th International Conference the Experience of Designing and Application on CAD Systems in Microelectronics (CADSM), pp. 350–352 (2011)
Mulesa, O., Geche, F., Batyuk, A., Buchok, V., Voloshchuk, V.: Information technology for time series forecasting with considering fuzzy expert evaluations
Geche, F., Mulesa, O., Geche, S., Vashkeba, M.: Development the method of synthesis of predictive scheme based on the basic forecasting models. Technol. Audit Reserves Prod. 3(2(23)), 36–41 (2015). https://doi.org/10.15587/2312-8372.2015.44932
Geche, F., Mulesa, O., Myronuyk, I., Vashkeba, M.: Prediction the quantitative characteristics of officially registered HIV-infected people in the region. Technol. Audit Reserves Prod. 4(2(24)), 34–39 (2015). https://doi.org/10.15587/2312-8372.2015.47907
Geche, F., Batyk, A., Mulesa, O., Vashkeba, M.: Development of effective time series forecasting model. Int. J. Adv. Res. Comput. Eng. Technol. (IJARCET) 4(12), 4377–4386 (2015)
Mulesa, O.: Methods of considering subjective character of input data for the tasks of voting. Eastern Eur. J. Enterp. Technol. 1(3(73)), 20–25 (2015). https://doi.org/10.15587/1729-4061.2015.36699
Orlovskyi, S.A.: Decision Making with Fuzzy Initial Information. Nauka, Moscow (1981)
Gnatienko, G., Snityuk, V.: Experts’ Technology of Decision Making. Makaut, Kiev (2008)
Korchenko, O.G., Hornitskyy, D.A., Zaharchuk, T.G.: Research priori estimation methods to implement an expert examination in the field of information security. Data Prot. 12 (4(49)) (2010). http://jrnl.nau.edu.ua/index.php/ZI/article/view/1976/1967
Kolpakova, T.A.: Determination of the competence of experts in making group decisions. Radioelektronika, computer science, upravlinnya 1(24), 40–43 (2011)
Snityuk, V. E.: Models and methods of determining the competence of experts on the basis of unbiasedness axiom. News CHITI, vol. 4, pp. 121–126 (2000)
Shanteau, J.: Competence in experts: the role of task characteristics. Organ. Behav. Hum. Decis. Process. 53(2), 252–266 (1992)
Uyomov, A.: System Approach and General Theory of Systems. Mysl, Moscow (1978)
Timchenko, A.A.: Fundamentals of System Design and System Analysis of Complex Objects. Lybed, Đšiev (2000)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Mulesa, O., Geche, F., Batyuk, A., Buchok, V. (2018). Development of Combined Information Technology for Time Series Prediction. In: Shakhovska, N., Stepashko, V. (eds) Advances in Intelligent Systems and Computing II. CSIT 2017. Advances in Intelligent Systems and Computing, vol 689. Springer, Cham. https://doi.org/10.1007/978-3-319-70581-1_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-70581-1_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70580-4
Online ISBN: 978-3-319-70581-1
eBook Packages: EngineeringEngineering (R0)