Forecasting tourism demand by extracting fuzzy Takagi-Sugeno rules from trained SVMs

Tourism demand forecasting has attracted substantial interest because of the significant economic contributions of the fast-growing tourism industry. Although various quantitative forecasting techniques have been widely studied, highly accurate and understandable forecasting models have not been developed. The present paper proposes a novel tourism demand forecasting method that extracts fuzzy Takagi-Sugeno （T-S） rules from trained SVMs. Unlike previous approaches, this study uses fuzzy T-S models extracted from the outputs of trained SVMs on tourism data. Owing to the symbolic fuzzy rules and the generalization ability of SVMs, the extracted fuzzy T-S rules exhibit high forecasting accuracy and include understandable pre-condition parts for practitioners. Based on the tourism demand forecasting problem in Hong Kong SAR, China as a case study, empirical findings on tourist arrivals from nine overseas origins reveal that the proposed approach performs comparably with SVMs and can achieve better prediction accuracy than other forecasting techniques for most origins. The findings demonstrated that decision makers can easily interpret fuzzy T-S rules extracted from SVMs. Thus, the approach is highly beneficial to tourism market management. This finding demonstrates the excellent scientific and practical values of the proposed approach in tourism demand forecasting.

Tourism demand forecasting and tourists’search behavior:evidence from segmented Baidu search volume

Given the importance of web search volume for reflecting tourists'preferences for certain tourism services and destinations,incorporating these data into forecasting models can significantly improve forecasting performance.This study enriches the literature on tourism demand forecasting and tourists'search behavior through segmented Baidu search volume data.First,this study divides Baidu search volume data based on volume sources and periods.Then,by analyzing the most relevant keywords in tourism demand in different segments,this study captures the dynamic characteristics of tourist search behavior.Finally,this study adopts a series of econometric and machine learning models to further improve the performance of tourism demand and forecasting.The findings indicate that tourists’search behavior has changed significantly with the prevalence and popularization of 4G technology and suggest that search volume improves forecasting performance,especially search volume on mobile terminals,from 2014M1–2019M12.

Interpretable Tourism Demand Forecasting with Two-Stage Decomposition and Temporal Fusion Transformers

This paper proposes a novel interpretable tourism demand forecasting framework that considers the impact of the COVID-19 pandemic by using multi-source heterogeneous data,namely,historical tourism volume,newly confirmed cases in tourist origins and destinations,and search engine data.This paper introduces newly confirmed cases in tourist origins and tourist destinations to forecast tourism demand and proposes a new two-stage decomposition method called ensemble empirical mode decomposition-variational mode decomposition to deal with the tourist arrival sequence.To solve the problem of insufficient interpretability of existing tourism demand forecasting,this paper also proposes a novel interpretable tourism demand forecasting model called JADE-TFT,which utilizes an adaptive differential evolution algorithm with external archiving(JADE)to intelligently and efficiently optimize the hyperparameters of temporal fusion transformers(TFT).The validity of the proposed prediction framework is verified by actual cases based on Hainan and Macao tourism data sets.The interpretable experimental results show that newly confirmed cases in tourist origins and tourist destinations can better reflect tourists'concerns about travel in the post-pandemic era,and the two-stage decomposition method can effectively identify the inflection point of tourism prediction,thereby increasing the prediction accuracy of tourism demand.