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Time Series Forecasting Using A Moving Average Model For Extrapolation Of Number Of Tourist

Author

Listed:
  • Zoran Ivanovski

    (University of Tourism and Management in Skopje)

  • Ace Milenkovski

    (University of Tourism and Management in Skopje)

  • Zoran Narasanov

    (Vienna Insurance Group, Macedonia.)

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.

Suggested Citation

  • Zoran Ivanovski & Ace Milenkovski & Zoran Narasanov, 2018. "Time Series Forecasting Using A Moving Average Model For Extrapolation Of Number Of Tourist," UTMS Journal of Economics, University of Tourism and Management, Skopje, Macedonia, vol. 9(2), pages 121-132.
    • Handle: RePEc:ris:utmsje:0243
    as

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    References listed on IDEAS

    as
    1. Everette S. Gardner, Jr. & Ed. Mckenzie, 1985. "Forecasting Trends in Time Series," Management Science, INFORMS, vol. 31(10), pages 1237-1246, October.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. Nelson, Charles R. & Plosser, Charles I., 1982. "Trends and random walks in macroeconmic time series : Some evidence and implications," Journal of Monetary Economics, Elsevier, vol. 10(2), pages 139-162.
    4. Armstrong, J. Scott, 2006. "Findings from evidence-based forecasting: Methods for reducing forecast error," International Journal of Forecasting, Elsevier, vol. 22(3), pages 583-598.
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    Cited by:

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    2. Amare Tesfaw & Feyera Senbeta & Dawit Alemu & Ermias Teferi, 2021. "Value Chain Analysis of Eucalyptus Wood Products in the Blue Nile Highlands of Northwestern Ethiopia," Sustainability, MDPI, vol. 13(22), pages 1-25, November.

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    More about this item

    Keywords

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    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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