Tourism demand forecasting with spatiotemporal features

Tourism Demand Interval Forecasting Amid COVID-19: A Hybrid Model With a Modified Multi-Objective Optimization Algorithm

“…Interval forecasting can provide an expected range of future outcomes, therefore contributing to contingency planning ( Wu et al, 2021 ). Reliable interval forecasting results during the COVID-19 pandemic provide crucial guidance for balancing the recovery of the tourism industry and the control of the epidemic spread ( Li et al, 2022 ). Specifically, when the predicted tourism volume is low, stakeholders can optimize pricing strategies dynamically or enact attractive travel packages to draw more tourists.…”

Can multi-source heterogeneous data improve the forecasting performance of tourist arrivals amid COVID-19? Mixed-data sampling approach

“…The SPA test compares favorably to the reality check (RC) for data snooping, because it is more powerful and less sensitive to poor and irrelevant alternatives. It is widely used in tourism demand forecasting for forecasting model performance evaluation (Tang et al, 2021; Li et al, 2022d , Li et al, 2022c , Li et al, 2022b , Li et al, 2022a ). The null hypothesis of the SPA test is expressed mathematically as follows: where L i is the loss from model i and L bm is the loss from the benchmark model.…”

“…Tourism demand recovery forecasting has attracted interests of plenty researchers and various forecasting models have been leveraged to improve the forecasting accuracy. Examples include combined econometric and judgemental models ( Zhang et al, 2021 ); scenario-based judgemental forecasting models ( Kourentzes et al, 2021 ; Liu et al, 2021a , Liu et al, 2021b ; Qiu et al, 2021 ) artificial neural networks (ANNs) technique ( Li et al, 2022a , Li et al, 2022b , Li et al, 2022c , Li et al, 2022d ) and expert judgement-based probabilistic forecasting models ( Athanasopoulos et al, 2022 ).…”

“…Various forecasting models have been leveraged to improve the forecasting accuracy. Examples include combined econometric and judgemental models ( Li et al, 2022a , Li et al, 2022b , Li et al, 2022c , Li et al, 2022d ; Zhang et al, 2021 ); scenario-based judgemental forecasting models ( Kourentzes et al, 2021 ; Qiu et al, 2021; Liu, Vici, et al, 2021); artific ial neural networks (ANNs) technique ( Li et al, 2022a , Li et al, 2022b ) and expert judgement-based probabilistic forecasting models ( Athanasopoulos, Hyndman, Kourentzes, & O'Hara-Wild, 2022 ).…”

Forecasting Tourism in the EU after the COVID-19 Crisis

“…A different approach was adopted by Bi et al (2021) , who introduce a forecasting model based on deep learning with time series imaging. Li et al (2022) emphasize the importance of spatiotemporal features in tourism demand forecasting and develop a deep learning model applicable to data with varying time granularities and periods (i.e., before and during COVID-19). The role of disaggregated search data in improving tourism forecasts is investigated in the case of Sri Lanka by Wickramasinghe and Ratnasiri (2021) , yet the main merit of their study is estimation of foregone economic benefits due to the pandemic.…”