A weighted fuzzy integrated time series for forecasting tourist arrivals

Abstract

Literature reviews show that the most commonly studied fuzzy time series models for the purpose of forecasting is first order. In such approaches, only the first lagged variable is used when constructing the first order fuzzy time series model. Therefore, such approaches fail to analyze accurately trend and seasonal time series which is an important class in time series models. In this paper, a weighted fuzzy integrated time series is proposed in order to analyze trend and seasonal data and data are taken from tourist arrivals series. The proposed approach is based on differencing concept as data preprocessing method and weighted fuzzy time series. The order of this model is determined by utilizing graphical order fuzzy relationship. Four data sets about the monthly number of tourist arrivals to Indonesia via four main gates are selected to illustrate the proposed method and compare the forecasting accuracy with classical time series models. The results of the comparison in test data show that the weighted fuzzy integrated time series produces more precise forecasted values than those classical time series models.