Short-term forecasting of Japanese tourist inflow to South Korea using Google trends data

We utilize the Internet search data from Google Trends to provide short-term forecasts for the inflow of Japanese tourists to South Korea. We construct the Google variable in a systematic way by combining keywords to minimize mean squared or mean absolute forecasting errors. We augment the Google variable to the standard time-series forecasting models and compare their forecasting accuracies. We find that Google-augmented models perform much better than the standard time-series models in terms of short-term forecasting accuracy. In particular, Google models show better out-of-sample forecasting performance than in-sample forecasting.     

Forecasting tourist arrivals in Barbados

Importance of forecasting in tourism is not a controversial issue. Recently, there has been increased attention on forecasting models in tourism. The value of a forecasting model depends on the accuracy of out-of-sample forecasts. At present, there is no indication as to which model or class of models is suitable for forecasting tourism. This paper specifies, estimates, and validates an ARIMA model for forecasting long-stay visitors in Barbados. The accuracy of the short-term forecasts surpasses most recent forecasting studies. The implication of the study is that customized model building may be highly rewarding in terms of accurate forecasts compared to standard or simple methods.     

Forecasting Demand for Australian Passports

We forecast demand for Australian passports using a number of univariate and multivariate forecasting models, and assess their relative predictive ability over a number of forecasting horizons and evaluation measures. Our key result is to use different forecasting models for predicting passport demand in the short- versus medium- to long-run. Specifically, to forecast Adult-and-Senior passport demand in the short-term (i.e. up to 12 months) univariate ARIMA models are preferred, while for the longer term forecasts multivariate models with exogenous variables outperform, although only marginally. To forecast passport demand for Minors (less than 18 years old) ARIMA models perform well both in the short-term and the long-term, although ARIMA with explanatory variables outperforms slightly.     

Directional bias in vacation travel

Vacation travel between regions is usually asymmetrical. This causes a problem when one tries to use a gravity model to forecast inter-regional travel. Although researchers have adopted strategies for coping with this problem, none of these strategies adequately resolve the complication of asymmetrical flows. This paper presents a measure of directional bias that accounts for unequal flows between two regions, and applies it to inter-provincial flows in Canada for the years 1968 to 1978. The directional bias appears to be stable over the period considered, and the index can be easily incorporated into forecasting models. Its use will allow researchers to add greater precision and power to their predictive models.     

Forecasting tourism revisited

This article examines the accuracy and efficiency of forecasting techniques by applying time series regression to forecasting visitor arrivals. Past studies have shown that simpler time series techniques perform as well or better than complex forecasting models. An assessment of visitor forecasts developed at regional, destination and individual market levels suggests that time series regression performs well in producing annual forecasts of visitors which can also serve as a baseline for evaluating the net returns from applying more complex techniques. Tourism managers should appreciate the usefulness of simpler formal methods in developing forecasts of visitors.     

Forecasting China's Monthly Inbound Travel Demand

Summary

China is currently expecting a growth in inbound travel demand as the result of China's “open door policy,” participation in World Trade Organization (WTO), success in hosting the Olympics in Beijing in the year 2008 and political stability. This paper focused on two issues: (1) forecasting China's monthly inbound travel demand and (2) seasonally and seasonal ARIMA model selection for monthly tourism time-series. In this paper following seasonal ARIMA models were considered: the seasonal ARIMA model with first differences and 11 seasonal dummy variables, the conventional seasonal ARIMA model with first and the fourth differences. In order to select the best forecasting model, finally both seasonal ARIMA models were compared with the AR model with fourth differences, the basic structural model (BSM) and the naive “No Change” model. In the one-step ahead forecasting comparison, the conventional seasonal ARIMA model with first and the fourth differences becomes the best forecasting model for both inbound foreign visitor demand and total visitor demand. This may be due to the nature of monthly seasonal variations in visitor arrivals, which is less marked. Our forecasts indicate that China foreign visitor arrivals and total visitor arrivals are expected to grow by 14% and 27% respectively from 2002 to 2005.     

Forecasting tourist arrivals with machine learning and internet search index

Previous studies have shown that online data, such as search engine queries, is a new source of data that can be used to forecast tourism demand. In this study, we propose a forecasting framework that uses machine learning and internet search indexes to forecast tourist arrivals for popular destinations in China and compared its forecasting performance to the search results generated by Google and Baidu, respectively. This study verifies the Granger causality and co-integration relationship between internet search index and tourist arrivals of Beijing. Our experimental results suggest that compared with benchmark models, the proposed kernel extreme learning machine (KELM) models, which integrate tourist volume series with Baidu Index and Google Index, can improve the forecasting performance significantly in terms of both forecasting accuracy and robustness analysis.     

Forecasting international city tourism demand for Paris: Accuracy of uni- and multivariate models employing monthly data

The purpose of this study is to compare the predictive accuracy of various uni- and multivariate models in forecasting international city tourism demand for Paris from its five most important foreign source markets (Germany, Italy, Japan, UK and US). In order to achieve this, seven different forecast models are applied: EC-ADLM, classical and Bayesian VAR, TVP, ARMA, and ETS, as well as the naïve-1 model serving as a benchmark. The accuracy of the forecast models is evaluated in terms of the RMSE and the MAE. The results indicate that for the US and UK source markets, univariate models of ARMA(1,1) and ETS are more accurate, but that multivariate models are better predictors for the German and Italian source markets, in particular (Bayesian) VAR. For the Japanese source market, the results vary according to the forecast horizon. Overall, the naïve-1 benchmark is significantly outperformed across nearly all source markets and forecast horizons.     

Review of tourism forecasting research with internet data

Internet techniques significantly influence the tourism industry and Internet data have been used widely used in tourism and hospitality research. However, reviews on the recent development of Internet data in tourism forecasting remain limited. This work reviews articles on tourism forecasting research with Internet data published in academic journals from 2012 to 2019. Then, the findings ae synthesized based on the following Internet data classifications: search engine, web traffic, social media, and multiple sources. Results show that among such classifications, search engine data are most widely incorporated into tourism forecasting. Time series and econometric forecasting models remain dominant, whereas artificial intelligence methods are still developing. For unstructured social media and multi-source data, methodological advancements in text mining, sentiment analysis, and social network analysis are required to transform data into time series for forecasting. Combined Internet data and forecasting models will help in improving forecasting accuracy further in future research.     

Impact of decomposition on time series bagging forecasting performance

Time series bagging has been deemed an effective way to improve unstable modelling procedures and subsequent forecasting accuracy. However, the literature has paid little attention to decomposition in time series bagging. This study investigates the impacts of various decomposition methods on bagging forecasting performance. Eight popular decomposition approaches are incorporated into the time series bagging procedure to improve unstable modelling procedures, and the resulting bagging methods' forecasting performance is evaluated. Using the world's top 20 inbound destinations as an empirical case, this study generates one-to eight-step-ahead tourism forecasts and compares them against benchmarks, including non-bagged and seasonal naïve models. For short-term forecasts, bagging constructed via seasonal extraction in autoregressive integrated moving average time series decomposition outperforms other methods. An autocorrelation test shows that efficient decomposition reduces variance in bagging forecasts.     

Forecasting hotel-industry performance

This paper provides a method which may be used by hospitality managers to forecast the annual hotel-industry occupancy rate for their respective localities. An empirical application of the method demonstrates that it can generate reasonably accurate forecasts of annual industry performance and can be useful to managers in their evaluation of the future competitive environment. An assessment of the influence of key variables upon forecast accuracy suggests that market research on trends in visitor characteristics would be useful for longer-term forecasting.     

Initially Testing an Improved Extrapolative Hotel Room Occupancy Rate Forecasting Technique

SUMMARY

The existing time series forecasting models either capture the informationof the last few data in the data series or the entire data series is used for projecting future values. In other words, the time series forecasting models are unable to take advantage of the last trend in the data series, which always have a direct influence on the estimated values. This paper proposes an improved extrapolative time series forecasting technique to compute future hotel occupancy rates. The performance of this new technique was tested with officially published room occupancy rates in Hong Kong. Forecasted room occupancy rates were compared with actual room occupancy rates in several accuracy performance dimensions. Empirical results indicate that the new technique is promising with reasonably good forecasting results.     

Forecasting tourism: a combined approach

In this article, we employ a combined seasonal nonseasonal ARIMA and sine wave nonlinear regression forecast model to predict international tourism arrivals, as represented by the number of world-wide visitors to Singapore. Compared with a similar study of the accuracy of international tourist arrivals forecasts by Chan (Journal of Travel Research, 1993, 31, 58–60)¹ and Chu (Journal of Travel Research, 1998, 36, 79–84)² using other univariate time series models, our proposed model has the smallest mean absolute percentage error.     

Learning and Ability to Pay

Summary

The decision whether to use time series or econometric methods to forecast demand is not clear. The literature reviewed only indicates that models should be simple and ideally be able to evolve over time. In 1997 two models were proposed to forecast the numbers from Britain skiing in Europe. The first used a learning curve approach and forecast a stationary market, whilst the second used a Varying Coefficient Model linking sales and ability to pay and forecast a gradually expanding market. This paper reviews the outcomes 1996-2000, the forecast performance of the two models and the stability of the structure of both when updated. It unequivocally suggests that the learning curve approach produced better forecasts. In the penultimate section a model that attempts to combine both approaches is developed. In this context the role of “historic” data is discussed. The paper concludes that the best forecasting approach will depend upon whether the market is stable and that the weight given to data must reflect the information content of that data.     

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

The coronavirus disease (COVID-19) pandemic has already caused enormous damage to the global economy and various industries worldwide, especially the tourism industry. In the post-pandemic era, accurate tourism demand recovery forecasting is a vital requirement for a thriving tourism industry. Therefore, this study mainly focuses on forecasting tourist arrivals from mainland China to Hong Kong. A new direction in tourism demand recovery forecasting employs multi-source heterogeneous data comprising economy-related variables, search query data, and online news data to motivate the tourism destination forecasting system. The experimental results confirm that incorporating multi-source heterogeneous data can substantially strengthen the forecasting accuracy. Specifically, mixed data sampling (MIDAS) models with different data frequencies outperformed the benchmark models.     

Tourism forecast combination using the CUSUM technique

Forecast combination in tourism has emerged as an important research area due to its relevance to tourism decision making. This paper further investigates the impact of forecast combination on forecast accuracy by applying a quadratic programming approach to determine the combination weights for individual forecasts. In particular, we introduce three novel ideas which have not been found in previous tourism forecasting studies. First, we introduce a quality control technique, CUSUM, to determine the time for updating the weights. Next, we develop a hybrid method (using quadratic programming) to combine the forecasts to reduce forecasting errors. Thirdly, we investigate whether different performance measures yield different results. Thus, instead of comparing different weighting methods using only one performance measure, we introduce several indicators for performance comparisons. The empirical results suggest that the controlled weighting method both saves time in updating the combination weights and improves the overall performance of the combined forecasts. The method is also easy-to-implement and should be used to improve forecasting accuracy in practice.     

Convention and Conference Facilities

Abstract

A rigorous statistical analysis indicates that large group bookings are a dominant source for errors in convention and conference facilities. This study demonstrates that the accuracy of the quantitative forecast can benefit from human judgment when an explicit structured process is applied to the judgmental adjustments. It develops and fits a correcting model that simulates managers' predictions. The results suggest that this approach can improve the accuracy of quantitative forecasting models when applied and calibrated to the hotel's specific characteristics.     

State tourism in China and USA

Schuchat, Molly G., “State Tourism in China and USA,” Annals of Tourism Research, October/December 1979, VI(4):425–434. This paper discusses the experiences available to most visitors to the Peoples Republic of China and to those visitors to the United States of America who are cultural exchange grantees. In the United States the Department of State contracts out the programming and direction of its officially invited guests to non-profit agencies that work in cooperation with a nationwide network of local volunteers. Almost all visitors to China, no matter who paid for their trip, were treated as official guests of the country, until 1978. It is only in the last year that they have been considered tourists at all. The range of contacts and experiences offered in both countries have a great deal of similarity. One focus of the paper is on what these guests (or any others not so similarly shepherded) are able to learn of countries not their own through exposure to public life. The material was gathered on visits to China in January, 1977 and September, 1978, and in interviews with programming and interpreting staffs, volunteers and grantees in Washington, D.C.     

百度指数与旅游景区游客量的关系及预测研究——以北京故宫为例

网络搜索数据记录了用户的搜索关注与需求,为研究旅游经济行为提供了必要数据基础。文章基于百度指数,以北京故宫为例,利用计量经济学中的协整理论和格兰杰因果关系分析了百度关键词与北京故宫实际游客量间的关系,建立了没有百度关键词和加入百度关键词的两种预测模型并进行了预测精度比较。结果表明：故宫实际游客量与百度关键词存在长期均衡关系和格兰杰因果关系：加入百度关键词后的自回归分布滞后模型的样本期内的预测精度比没有百度关键词的ARMA模型提高了12．4％,样本期外的预测精度提高了14．5％。运用带有百度关键词的模型可以实现利用当天及滞后1～2天的百度指数数据预测故宫当天的游客量,不仅增强了预测的时效性,还可以更加及时、准确地为故宫景区管理部门提供决策的依据。