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  • 标题:FORECASTING FOREIGN TOURIST ARRIVALS TO INDIA USING TIME SERIES MODELS
  • 本地全文:下载
  • 作者:Shalini Chandra ; Kriti Kumari
  • 期刊名称:Journal of Data Science
  • 印刷版ISSN:1680-743X
  • 电子版ISSN:1683-8602
  • 出版年度:2018
  • 卷号:16
  • 期号:4
  • 页码:707-722
  • DOI:10.6339/JDS.201810_16(4).00003
  • 出版社:Tingmao Publish Company
  • 摘要:This study aims to compare various quantitative models to forecast monthly foreign tourist arrivals (FTAs) to India. The models which are considered here include vector error correction (VEC) model, Naive I and Naive II models, seasonal autoregressive integrated moving average (SARIMA) model and Grey models. A model based on combination of single forecast values using simple average (SA) method has also been applied. The forecasting performance of these models have been compared under mean absolute percentage error (MAPE) and U-statistic (Ustat) criteria. Empirical findings suggest that the combination model gives better forecast of FTAs to India relative to other individual time series models considered here.
  • 关键词:Foreign tourist arrivals;Time series models;Forecast comparisons.
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