:: International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies
FEATURE PEER-REVIEWED ARTICLE
Alfira Kumratova, Elena Popova, (Department of Information Systems, Kuban State Agrarian University named after I.T. Trubilin, Krasnodar, RUSSIA),
Sergey Kurnosov, (Department of Computer Technologies and Systems, Kuban State Agrarian University named after I.T. Trubilin, Krasnodar, RUSSIA),
Valery Kondratiev, Natalia Kurnosova(Department of Information Systems, Kuban State Agrarian University named after I.T. Trubilin, Krasnodar, RUSSIA).
Disciplinary: Computer Technologies & Information Systems, Applied Mathematics; Hospitality and Tourism Management.
Keywords: Tourism economics; Tourist management; Phase analysis; Decomposed time series; R/S-analysis; Tourist forecast; Linear cellular automaton; Russian tourism.
This article adopts a complex methodology for predicting the dynamics of the decomposition time series of the tourist flow. Its peculiarities lie in the joint use of both classical and new "nonlinear" statistics. The proposed and tested methods are presented in the form of a pre-forecast and forecast model for assessing the trend stability of time series of the tourist flow and obtaining a forecast. The methods of nonlinear dynamics have been tested including the Hurst normalized range method, phase analysis, and a linear cellular automaton. The results of the analysis and forecast on real data of the tourist flow are presented in the form of the values of the lower level of modeling of tourist and recreational activities, which are input data for the models of the upper level, the level of management of tourist and recreational activities. A quantitative forecast of the magnitude of the tourist flow allows solving the issues of managing tourist and recreational activities, such as planning the employment of rooms.
Paper ID: 12A8P
Cite this article:
Kumratova, A., Popova, E., Kurnosov, S., Kondratiev, V., Kurnosova, N. (2021). Dynamics Data Prediction based on Time Series Decomposition: The Case of Tourist Flow Data. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies, 12(8), 12A8P, 1-10. http://doi.org/10.14456/ITJEMAST.2021.163