The adaptive fuzzy time series model with an application to Taiwan’s tourism demand

In this study, an adaptive fuzzy time series model for forecasting Taiwan’s tourism demand is proposed to further enhance the predicted accuracy. We first transfer fuzzy time series data to the fuzzy logic group, assign weights to each period, and then use the proposed adaptive fuzzy time series model for forecasting in which an enrollment forecasting values is applied to obtain the smallest forecasting error. Finally, an illustrated example for forecasting Taiwan’s tourism demand is used to verify the effectiveness of proposed model and confirmed the potential benefits of the proposed approach with a very small forecasting error MAPE and RMSE.

Research highlights
► We model an adaptive analysis method into fuzzy time series model for forecasting Taiwan’s tourism demand.
► We transform fuzzy time series data to fuzzy logic group, and assign weights to each period for the propsed adaptive fuzzy time series model.
► A numerical example for Taiwan’s toursim demand forecasting is given to confirmand show a best forecasting performance.