FORECAST OF TOURIST DEMAND IN UKRAINE ON A FAST-FUTURE PROSPECTS
Abstract
The process of formation of tourist demand was studied and autocorrelation and partial auto-correlation were calculated. Valued behavior of selective ACF and partial PACF, showing the hypothesis about the values of the parameters p and q. Due to the lack of data, several competing ARMA (1.1) and ARMA (2.0) models have been selected. Both models showed a good match with the data, the models are adequate and the errors are random, so the best model is chosen according to the AIC and BIC criterion. The remains of the selected model are checked for the absence of auto-correlation using the Lew Box test. For the selected best model, forecasts were projected for 5 periods ahead. From the forecast of the time series it is clear that the tourist demand in the next 5 years will decline.
References
http://www.ukrstat.gov.ua - Official statistical information of the State Statistics Committee of Ukraine
Box J. Analysis of time series. Forecast and management: Vol. 1 (Translated from English by A.A. Levshin) Moscow: Mir, 1974. p. 406
Venables W. N., Smith D. M. An Introduction to R [Electronic resource]. Access mode: http://www.e-booksdirectory.com/details.php?ebook=1791
Bidyuk P.I. Analysis of time series. / P.I. Bidyuk, V.D. Romanenko O.L. Tymoshchuk - Kyiv: "Polytechnic", NTUU "KPI", 2013. - p. 600
Minayev Yu.M., Aponasenko D.V. Intelligent technologies of forecasting of time series on the basis of tensor invariants // Problems of informatization and management: Collection of sciences. Ave - K.: NAU, 2009. - Issue number 2 (26). - p. 105-113
Prisenko G.V., Ravikovich E.I. Prediction of Economic Processes: Textbook / Prisenko G.V., Ravikovich E.I. - Kyiv: KNEU, 2005. - p. 382
Views:
99
Downloads:
131
Copyright (c) 2018 Authors
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles are published in open-access and licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Hence, authors retain copyright to the content of the articles.
CC BY 4.0 License allows content to be copied, adapted, displayed, distributed, re-published or otherwise re-used for any purpose including for adaptation and commercial use provided the content is attributed.
