A review of research on tourism demand forecasting: Launching the Annals of Tourism Research Curated Collection on tourism demand forecasting
This study reviews 211 key papers published between 1968 and 2018, for a better
understanding of how the methods of tourism demand forecasting have evolved over time …
understanding of how the methods of tourism demand forecasting have evolved over time …
New developments in tourism and hotel demand modeling and forecasting
Purpose The purpose of this paper is to review recent studies published from 2007 to 2015
on tourism and hotel demand modeling and forecasting with a view to identifying the …
on tourism and hotel demand modeling and forecasting with a view to identifying the …
Urban water demand prediction for a city that suffers from climate change and population growth: Gauteng province case study
The proper management of a municipal water system is essential to sustain cities and
support the water security of societies. Urban water estimating has always been a …
support the water security of societies. Urban water estimating has always been a …
Forecasting tourism demand with composite search index
Researchers have adopted online data such as search engine query volumes to forecast
tourism demand for a destination, including tourist numbers and hotel occupancy. However …
tourism demand for a destination, including tourist numbers and hotel occupancy. However …
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 …
to 2017. Forecasting models are reviewed in three categories: econometric, time series and …
A bi‐level ensemble learning approach to complex time series forecasting: Taking exchange rates as an example
J Hao, QQ Feng, J Li, X Sun - Journal of Forecasting, 2023 - Wiley Online Library
Forecasting complex time series faces a huge challenge due to its high volatility. To improve
the accuracy and robustness of prediction, this paper proposes a bi‐level ensemble learning …
the accuracy and robustness of prediction, this paper proposes a bi‐level ensemble learning …
Forecasting tourism demand with denoised neural networks
Abstract The automated Neural Network Autoregressive (NNAR) algorithm from the forecast
package in R generates sub-optimal forecasts when faced with seasonal tourism demand …
package in R generates sub-optimal forecasts when faced with seasonal tourism demand …
Forecasting with big data: A review
H Hassani, ES Silva - Annals of Data Science, 2015 - Springer
Big Data is a revolutionary phenomenon which is one of the most frequently discussed
topics in the modern age, and is expected to remain so in the foreseeable future. In this …
topics in the modern age, and is expected to remain so in the foreseeable future. In this …
A Kolmogorov-Smirnov based test for comparing the predictive accuracy of two sets of forecasts
H Hassani, ES Silva - Econometrics, 2015 - mdpi.com
This paper introduces a complement statistical test for distinguishing between the predictive
accuracy of two sets of forecasts. We propose a non-parametric test founded upon the …
accuracy of two sets of forecasts. We propose a non-parametric test founded upon the …
A decomposition-ensemble approach for tourism forecasting
G Xie, Y Qian, S Wang - Annals of Tourism Research, 2020 - Elsevier
With the frequent occurrence of irregular events in recent years, the tourism industry in some
areas, such as Hong Kong, has suffered great volatility. To enhance the predictive accuracy …
areas, such as Hong Kong, has suffered great volatility. To enhance the predictive accuracy …