Artikel

Time series forecasting using a moving average model for extrapolation of number of tourist

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.

Language
Englisch

Bibliographic citation
Journal: UTMS Journal of Economics ; ISSN: 1857-6982 ; Volume: 9 ; Year: 2018 ; Issue: 2 ; Pages: 121-132

Classification
Wirtschaft
Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
Subject
seasonality
trend
regression
forecasting
centered moving average

Event
Geistige Schöpfung
(who)
Ivanovski, Zoran
Milenkovski, Ace
Narasanov, Zoran
Event
Veröffentlichung
(who)
University of Tourism and Management
(where)
Skopje
(when)
2018

Handle
Last update
10.03.2025, 11:46 AM CET

Data provider

This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.

Object type

  • Artikel

Associated

  • Ivanovski, Zoran
  • Milenkovski, Ace
  • Narasanov, Zoran
  • University of Tourism and Management

Time of origin

  • 2018

Other Objects (12)