2018, Vol. 3, Issue 2, Part E
Forecasting foreign tourist arrivals in India using time series models
Author(s): Shalini Chandra and Kriti Kumari
Abstract: This study aims to compare various time series models to forecast monthly foreign tourist arrivals to India. The models which are considered here include Naive I & Naive II, seasonal autoregressive integrated moving average (SARIMA) and Grey models. The forecasting performance of these models has been compared under mean absolute percentage error (MAPE), U-statistic and turning point analysis (TPA) criteria. Empirical findings show that Naive I gives better forecast of foreign tourist arrivals to India relative to other time series models under MAPE and U-statistic criteria. In addition, SARIMA is found to be better model as compared to other models according to TPA criterion.
Pages: 338-342 | Views: 1081 | Downloads: 24Download Full Article: Click HereHow to cite this article:
Shalini Chandra, Kriti Kumari. Forecasting foreign tourist arrivals in India using time series models. Int J Stat Appl Math 2018;3(2):338-342.
