Forecasting Short Time-Series Tourism Demand with Artificial Intelligence Models

Gongmei Yu · Zvi Schwartz

110 citations0/0 files storedJournal of Travel Research

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Forecasting Techniques and Applications Stock Market Forecasting Methods Grey System Theory Applications

Abstract

This study examines the forecast accuracy of fuzzy time series and grey theory in predicting annual U.S. tourist arrivals. The performance of the two artificial intelligence (AI) models is compared to that of two simple methods—double moving average and double exponential smoothing. The rigorous testing approach includes a large sample stratified to adequately represent four generic trend patterns: a rolling short-term forecast, a large holdout sample, models fitting with both equal number of years and optimal number of years, and tests of statistical significance using Wilcoxon’s signed-ranks nonparametric test. This study’s findings indicate, in contrast to recent findings, that the complicated models are not likely to generate a more accurate forecast than the simple traditional models. Given the notable cost associated with these AI forecasting methods, our finding of no significant accuracy advantage suggests that tourism forecasters should not rush to adopt these two methods without careful consideration.

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1982Control problems of grey systems 2000The M3-Competition: results, conclusions and implications 1981On Market Timing and Investment Performance. II. Statistical Procedures for Evaluating Forecasting Skills 1993Forecasting enrollments with fuzzy time series — Part I 2015On Market Timing and Investment Performance Part II: Statistical Procedures for Evaluating Forecasting Skills 2000Out-of-sample tests of forecasting accuracy: an analysis and review 1994Forecasting enrollments with fuzzy time series — part II 2016Evaluation of Economic Forecasts

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