Tourism Forecasting Using Chen and Singh Fuzzy Time Series Models - Vivianti Vivianti; Aswi Aswi (Journal of Mathematics, Computations and Statistics, 2026)
The tourism sector is one of the main drivers of the national economy, which experienced a significant decline due to the COVID-19 pandemic. In the post-pandemic era, the recovery of international tourist arrivals shows a positive trend, thus requiring accurate forecasting methods to support tourism policy planning. ARIMA method are less effective in handling nonlinear and fluctuating data. This study applies the Fuzzy Time Series (FTS) approach, specifically the Chen and Singh models, which are capable of managing data uncertainty and representing linguistic patterns adaptively. The purpose of this study is to compare the accuracy of both models using two interval determination approaches, namely the Sturges method and the mean-based method, in forecasting international tourist arrivals through Sultan Hasanuddin International Airport during the period from January 2023 to September 2025. The analytical steps include defining the universe of discourse, performing fuzzification, constructing fuzzy logical relationships (FLR) and fuzzy logical relationship groups (FLRG), and applying defuzzification to obtain forecasted values. The forecasting accuracy was evaluated using the Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The results show that the choice of interval determination method significantly affects forecasting performance, with the mean-based method producing more detailed and accurate intervals. Based on the evaluation, the FTS Singh model demonstrated the best performance, with MAPE of 2.16% and RMSE of 31.05, outperforming the Chen model under both interval approaches. Therefore, the combination of the FTS Singh model with the mean-based interval method is recommended as the optimal approach for forecasting post-pandemic international tourist arrivals, as it can capture fluctuating data patterns more precisely and consistently.