Dr Xiaoying (Eden) Jiao

PhD researcher

Academic and research departments

School of Hospitality and Tourism Management.


Xiaoying (Eden) Jiao is a PhD student in the School of Hospitality and Tourism Management at the University of Surrey, Guildford, United Kingdom. Before joining the University of Surrey, she received her bachelor degree in mathematical science with a concentration on operation research and statistics in 2017 at Carnegie Mellon University, Pittsburgh, United States. Her main research interests include tourism demand forecasting and applications of time series and econometric models.  

My qualifications

Bsc in Mathematical Science with a concentration in Operations Research and Statistics
Carnegie Mellon University


Research interests



Eden Xiaoying Jiao, Jason Li Chen (2018) Tourism forecasting: A review of methodological developments over the last decade

This study reviewed 72 studies in tourism demand forecasting during the period from 2008 to 2017. Forecasting models are reviewed in three categories: econometric, time series and artificial intelligence (AI) models. Econometric and time series models that have already been widely used before 2007 remained their popularity and were more often used as benchmark models for forecasting performance evaluation and comparison with respect to new models. AI models are rapidly developed in the past decade and hybrid AI models are becoming a new trend. And some new trends with regard to the three categories of models have been identified, including mixed frequency, spatial regression and combination and hybrid models. Different combination components and combination techniques have been discussed. Results in different studies proved superiority of combination forecasts over average single forecasts performance.

Gang Li, Xiaoying Jiao (2020) Tourism forecasting research: a perspective article

The purpose of this paper is to provide a short review of tourism forecasting literature and general summary of the trends and developments in tourism forecasting and point out directions for future research in the next 75 years.

Xiaoying Jiao, Gang Li, Jason Li Chen (2020) Forecasting international tourism demand: a local spatiotemporal model

This study investigates whether tourism forecasting accuracy is improved by incorporating spatial dependence and spatial heterogeneity. One- to three-step-ahead forecasts of tourist arrivals were generated using global and local spatiotemporal autoregressive models for 37 European countries and the forecasting performance was compared with that of benchmark models including autoregressive moving average, exponential smoothing and Naïve 1 models. For all forecasting horizons, the two spatial models outperformed the non-spatial models. The superior forecasting performance of the local model suggests that the full reflection of spatial heterogeneity can improve the accuracy of tourism forecasting.