TIME SERIES FORECASTING USING A MOVING AVERAGE MODEL FOR EXTRAPOLATION OF NUMBER OF TOURIST

Zoran Ivanovski, Ace Milenkovski () and Zoran Narasanov
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Zoran Ivanovski: University of Tourism and Management in Skopje
Ace Milenkovski: University of Tourism and Management in Skopje
Zoran Narasanov: Vienna Insurance Group, Macedonia.

UTMS Journal of Economics, 2018, vol. 9, issue 2, 121-132

Abstract: Time series is a collection of observations made at regular time intervals and its analysis refers to problems in correlations among successive observations. Time series analysis is applied in all areas of statistics but some of the most important include macroeconomic and financial time series. In this paper we are testing forecasting capacity of the time series analysis to predict tourists’ trends and indicators. We found evidence that the time series models provide accurate extrapolation of the number of guests, quarterly for one year in advance. This is important for appropriate planning for all stakeholders in the tourist sector. Research results confirm that moving average model for time series data provide accurate forecasting the number of tourist guests for the next year.

Keywords: seasonality; trend; regression; forecasting; centered moving average (search for similar items in EconPapers)
JEL-codes: C30 C32 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:ris:utmsje:0243

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