Tourism forecast combination using weighting schemes with flow information among component models

Abstract

Forecast combination is an effective way to improve the accuracy of tourism demand forecasting. The continuous development of forecast combination methods with high accuracy is inevitable to help tourism practitioners formulate more appropriate management strategies. This study investigated how tourism forecasting accuracy can be improved by treating combination forecasting as a multiple attribute decision making (MADM) problem. The proposed hybrid methods first yield single-model forecasts from grey models without considering the sample size and limiting the available data to satisfy any statistical properties. Given the effectiveness of PROMETHEE in MADM, which applies flows to gauge the intensity of the preference for one alternative over another, the flows among component models in a combination are then used to assess relative weights next. Finally, the flow-based weighting schemes are incorporated into the linear and nonlinear combinations of individual forecasts. After assessing the accuracy of the proposed methods with the inbound tourism demand in Taiwan, the results indicated that the proposed methods involving the integration of the flow-based weighting scheme into the Choquet fuzzy integral performed better than other benchmark forecast combination methods with different model combinations.