Tourism Demand Forecasting Research Articles

Pronóstico de la demanda turística de Chile basado en modelos lineales y no lineales estacionales

TIn this paper, the Chilean tourism demand experienced during the period 2000‑2018 has been modelled using a linear regression model with dichotomous variables (MRL) and an ARIMA model with a seasonal component (SARIMA). The results show that the SARIMA approach is more effective in replicating the non‑stationary, non‑linear behaviour and the presence of seasonality in the series, with the forecasts obtained from this model presenting a low rate of error, in this case ‑5.6% for outbound tourism and ‑5.9% for inbound tourism. In consequence, this approach may be an effective tool for forecasting forecast tourism demand in the short term, and support for planning and management in the sector in the face of fluctuations in tourism demand.

Modelling and Forecasting Inbound Tourism Demand to Croatia using Artificial Neural Networks: A Comparative Study

Tourism demand is the basis on which all commercial decisions concerning tourism ultimately depend. Accurate estimation of tourism demand is essential for the tourism industry because it can help reduce risk and uncertainty as well as effectively provide basic information for better tourism planning. The purpose of this study is to develop the optimal forecasting model that yields the highest accuracy when compared to the forecast performances of three different methods, namely Artificial Neural Network (ANN), Exponential Smoothing, and Box-Jenkins methods for forecasting monthly inbound tourist flows to Croatia. Prior studies have been applied to forecast tourism demand to Croatia based on time series models and casual methods. However, the monthly and comparative tourism demand forecasting studies using ANNs are still limited, and this paper aims to fill this gap. The number of monthly foreign tourist arrivals to Croatia covers the period between January 2005-December 2019 data were used to build optimal forecasting models. Forecasting performances of the models were measured by Mean Absolute Percentage Error (MAPE) statistics. As a result of the experiments carried out, when compared to the forecasting performances of various models, 12 lagged ANN models, which have [4-3-1] architecture, were seen to perform best among all models applied in this study. Considering both the empirical findings obtained from this study and previous studies on tourism forecasting, it can be seen that ANN models that do not have any negativities (such as over-training, faulty architecture, etc.) produce successful forecasting results when compared with results generated by conventional statistical methods.

Forecasting Foreign Visitors using SARIMAX Models with the Exogenous Variable of Demand Decrease

In this study, we consider the problem of forecasting the number of inbound foreigners visiting Korea. Forecasting tourism demand is an essential decision to plan related facilities and staffs, thus many studies have been carried out, mainly focusing on the number of inbound or outbound tourists. In order to forecast tourism demand, we use a seasonal ARIMA (SARIMA) model, as well as a SARIMAX model which additionally comprises an exogenous variable affecting the dependent variable, i.e., tourism demand. For constructing the forecasting model, we use a search procedure that can be used to determine the values of the orders of the SARIMA and SARIMAX. For the exogenous variable, we introduce factors that could cause the tourism demand reduction, such as the 9/11 attack, the SARS and MERS epidemic, and the deployment of THAAD. In this study, we propose a procedure, called Measuring Impact on Demand (MID), where the impact of each factor on tourism demand is measured and the value of the exogenous variable corresponding to the factor is determined based on the measurement. To show the performance of the proposed forecasting method, an empirical analysis was conducted where the monthly number of foreign visitors in 2019 were forecasted. It was shown that the proposed method can find more accurate forecasts than other benchmarks in terms of the mean absolute percentage error (MAPE).

Modeling and Forecasting of Tourism Demand in Malaysia

The pattern of foreign tourist demand to Malaysia is analysed and forecasted using time series method and non-linear technique. There are nine selected countries that contribute a lot to tourist arrivals to Malaysia, namely Australia, Brunei, China, Indonesia, India, Japan, the Philippines, South Korea, and the United Kingdom. Box-Jenkins time series method and Singular Spectrum Analysis are conducted and compared to study the best model to forecast the foreign tourist demand to Malaysia. Monthly data of tourism arrival in 1990 to 2014 were used and the forecasting were compared with 2015. Based on the results obtained, the forecasting model of Box-Jenkins time series method is the best model based on the percentage accuracy in forecasting the tourist demand to Malaysia.

Hierarchical pattern recognition for tourism demand forecasting

This study proposes a hierarchical pattern recognition method for tourism demand forecasting. The hierarchy consists of three tiers: the first tier recognizes the calendar pattern of tourism demand, identifying work days and holidays and integrating “floating holidays.” The second tier recognizes the tourism demand pattern in the data stream for different calendar pattern groups. The third tier generates forecasts of future tourism demand. Evidence from daily tourist visits to three attractions in China shows that the proposed method is effective in forecasting daily tourism demand. Moreover, the treatment of “floating holidays” turns out to be more effective and flexible than the commonly adopted dummy variable approach.

International tourism demand forecasting with machine learning models: The power of the number of lagged inputs

This study explores how to select the optimal number of lagged inputs (NLIs) in international tourism demand forecasting. With international tourist arrivals at 10 European countries, the performances of eight machine learning models are evaluated using different NLIs. The results show that: (1) as NLIs increases, the error of most machine learning models first decreases rapidly and then tends to be stable (or fluctuates around a certain value) when NLIs reaches a certain cutoff point. The cutoff point is related to 12 and its multiples. This trend is not affected by the size of the test set; (2) for nonlinear and ensemble models, it is better to select one cycle of the data as the NLIs, while for linear models, multiple cycles are a better choice; (3) significantly different prediction results are obtained by different categories of models when the optimal NLIs are used.

The role of disaggregated search data in improving tourism forecasts: Evidence from Sri Lanka

ABSTRACT Formulation of effective policies to enhance the resilience of tourism following the COVID-19 pandemic essentially requires comprehensive empirical information on changes in tourism demand and associated economic costs. The paper makes a novel contribution to tourism literature by employing regionally and temporally disaggregated tourism data and Google search data in improving the accuracy of tourism forecasts. Further, the paper adopts two timeseries variables namely tourist arrivals and guest nights in order to understand the changes due to COVID-19 in tourism demand more comprehensively. Monthly data on international tourist arrivals, guest nights and Google trends from 2004 to 2019 are used to produce regionally disaggregated (Europe, Asia, the Pacific, America, Other) monthly tourism forecasts for Sri Lanka. We find that SARMAX models outperform the other models (ARIMA, ARIMAX, SARIMA) in forecasting tourism demand following COVID-19. Interestingly, the paper makes a further step in utilizing forecasts in estimating foregone economic benefits due to COVID-19 pandemic. We find a notable difference in estimated direct economic loss depending on the variable used in estimates. The percentage loss is 40% when arrival forecasts are used in estimates and 29% when guest night forecasts are used in estimates. This provides important policy implications for improving post-COVID tourism.

Modeling and Forecasting of Tourist Arrivals in Crete Using Statistical Models and Models of Computational Intelligence

In the past few decades, tourism has clearly become one of the most prominent economic trends for many countries. For many destinations, this trend will continue to rise, and tourism will become the most dynamic and fastest growing sector of the economy. Thus, the reliable and accurate forecasting of tourism demand is necessary in making decisions for effective and efficient planning of tourism policy. The objective of this paper is the modeling and forecasting the international tourist arrivals to four prefectures of Crete in the year 2012, based on the actual tourist arrivals data over the period 1993 – 2011, using one-step-ahead forecast. In particular, this paper presented a comparative study of time series forecasts of international travel demand for the four prefectures of Crete using a variety of statistical quantitative forecasting models along with neural networks and fuzzy models.

우리나라 관광경제학 연구의 동향과 방향

This study aims to analyze trends in tourism economics research published to tourism and hospitality areas, and suggest research directions in the future. To this end this study searched for “tourism economics” from the database of KCI(Korea Citation Index) by Korean Research Foundation during the periods of 2002∼2020 and implemented language network analysis and visualization. Research papers of tourism economics were broadly classified by forecasting tourism demand, economic impacts, and valuation, in which major key words were derived. Results show that Journal of Tourism Sciences published the largest research papers of tourism economics(81), followed by Journal of Tourism & Leisure Research(77), Korean Journal of Hospitality & Tourism(46), Korean Journal of Tourism Research(39), International Journal of Tourism & Hospitality Research(39), and Journal of Hospitality & Tourism Studies(15). With regard to detailed fields of research papers, forecasting tourism demand was dominant, but showed a decreasing trend. Recently research papers of economic impacts indicated more increasing trend than valuation. The results of language network analysis indicate that major key words included “tourism”, “demand”, “input-output model”, “time-series”, “CVM”, “festival”, “willingness to pay(WTP)”, and “tourism demand.” The visualized results of language network show that the major key word of “tourism” was highlighted with “WTP”, “festival”, “tourist”, “multiplier”, and “time-series” during the periods of 2002∼2014. On the other hand, the major key word of “demand” was highlighted with “festival”, “tourism demand”, and “tourist“ during the periods of 2015∼2020. When comparing two periods, valuation papers were dominant in the past, but recently papers related to tourism demand and economic impacts showed an increasing trend. Furthermore, studies on festival were show a more increasing trend.

Forecasting Monthly Tourism Demand Using Enhanced Backpropagation Neural Network

The accurate forecasting of monthly tourism demand can improve tourism policies and planning. However, the complex nonlinear characteristics of monthly tourism demand complicate forecasting. This study proposes a novel approach named ICPSO-BPNN that combines improved chaotic particle swarm optimization (ICPSO) with backpropagation neural network (BPNN) to forecast monthly tourism demand. ICPSO with chaotic initialization and two search strategies, sigmoid-like inertia weight, and linear acceleration coefficients is utilized to search for the appropriate initial connection weights and thresholds necessary to improve the performance of BPNN. Two comparative real-life examples and one extended example are adopted to verify the superiority of the proposed ICPSO-BPNN. Results show ICPSO-BPNN outperforms that of the basic BPNN, autoregressive integrated moving average model, support vector regression, and other popular existing models.

“A study in grey”: a critical reflection on scientific (mis)behaviors in quantitative inquiries

ABSTRACT Application of quantitative techniques in the study of tourism phenomena has gained momentum in the last two decades. Various quantitative methods allow researchers to investigate problems encompassing, for instance, tourists’ behaviors, destinations efficiency, or tourism demand forecasting. However, statistical apparatus in the hands of a professional can either reveal a great deal about the world or conversely, deceive thousands of people. Past research touching upon the subject of scientific misbehaviors focused primarily on answering questions of who?, when? or how often?, leaving the matter of how specifically? rather uninvestigated. In the current paper, a grey area between quantitative data modification and falsification is discussed with the intention to build greater awareness among undergraduate students about potential pitfalls related to the execution of quantitative inquiries. To increase transparency and credibility of future scientific endeavors, practical recommendations are proposed on how to work responsibly within the confines of quantitative tourism research.

Tourism Forecasting and Tackling Fluctuating Patterns: A Composite Leading Indicator Approach

Abstract A growing interest is surfacing toward the non-linearities in tourism demand forecasting. This paper aims to construct a tourism composite indicator to anticipate the cyclical movement for the tourism demand in Fiji. The time duration tackled in the current study is under the span of approximately two decades from 2000 to 2017. Apart from an indicator construction approach and turning point dating algorithm, we proposed a two regime Markov switching model to analyse the Fijian tourism cycle. The empirical results revealed the composite leading indicator accorded a signalling approach for Fijian tourism demand with an average lead time of 2.75 months. Furthermore, the prior-recognition of the tourism economic transition with adequate dating evaluation of the tourism cycle is obtained from the filtered probabilities through the Markov switching models.

Machine Learning in Internet Search Query Selection for Tourism Forecasting

Prior studies have shown that Internet search query data have great potential to improve tourism forecasting. As such, selecting the most relevant information from large amounts of search query data is crucial to enhancing forecasting accuracy and reducing overfitting; however, such feature selection methods have not been considered in the tourism forecasting literature. This study employs four machine learning–based feature selection methods to extract useful search query data and construct relevant econometric models. We examined the proposed methods based on monthly forecasting of tourist arrivals in Beijing, China, along with weekly forecasting of hotel occupancy in the city of Charleston, South Carolina, USA. Our findings indicate that the forecasting model with the selected search keywords outperformed the benchmark ARMAX model without feature selection in forecasting tourism demand and hotel occupancy. Therefore, machine learning methods can identify the most useful search query data to significantly improve forecasting accuracy in tourism and hospitality.

Knowledge mapping of tourism demand forecasting research

Utilizing a scientometric review of global trends and structure from 388 bibliographic records over two decades (1999–2018), this study seeks to advance the building of comprehensive knowledge maps that draw upon global travel demand studies. The study, using the techniques of co-citation analysis, collaboration network and emerging trends analysis, identified major disciplines that provide knowledge and theories for tourism demand forecasting, many trending research topics, the most critical countries, institutions, publications, and articles, and the most influential researchers. The increasing interest and output for big data and machine learning techniques in the field were visualized via comprehensive knowledge maps. This research provides meaningful guidance for researchers, operators and decision makers who wish to improve the accuracy of tourism demand forecasting.

Are Combined Tourism Forecasts Better at Minimizing Forecasting Errors?

This study, which was contracted by the European Commission and is geared towards easy replicability by practitioners, compares the accuracy of individual and combined approaches to forecasting tourism demand for the total European Union. The evaluation of the forecasting accuracies was performed recursively (i.e., based on expanding estimation windows) for eight quarterly periods spanning two years in order to check the stability of the outcomes during a changing macroeconomic environment. The study sample includes Eurostat data from January 2005 until August 2017, and out of sample forecasts were calculated for the last two years for three and six months ahead. The analysis of the out-of-sample forecasts for arrivals and overnights showed that forecast combinations taking the historical forecasting performance of individual approaches such as Autoregressive Integrated Moving Average (ARIMA) models, REGARIMA models with different trend variables, and Error Trend Seasonal (ETS) models into account deliver the best results.

Tourism Demand Forecasting: A Decomposed Deep Learning Approach

Tourism planners rely on accurate demand forecasting. However, despite numerous advancements, crucial methodological issues remain unaddressed. This study aims to further improve the modeling accuracy and advance the artificial intelligence (AI)-based tourism demand forecasting methods. Deep learning models that predict tourism demand are often highly complex and encounter overfitting, which is mainly caused by two underlying problems: (1) access to limited data volumes and (2) additional explanatory variable requirement. To address these issues, we use a decomposition method that achieves high accuracy in short- and long-term AI-based forecasting models. The proposed method effectively decomposes the data and increases accuracy without additional data requirement. In conclusion, this study alleviates the overfitting issue and provides a methodological contribution by proposing a highly accurate deep learning method for AI-based tourism demand modeling.

Forecasting tourism demand using fractional grey prediction models with Fourier series

Tourism demand forecasting has played an important role in supporting governments to devise development policies for travel and tourism. However, time series related to tourism often do not conform to statistical assumptions and feature significant temporal fluctuations. Because a Fourier series is often applied to oscillating sequences to remove noise, it is reasonable to develop a grey prediction model in conjunction with a Fourier series to forecast tourism demand. However, grey prediction models traditionally use one-order accumulation, treating each sample with equal weight, to identify regularities concealed in data sequences. Furthermore, when generating residuals from Fourier series, the prediction accuracy of the newly generated predicted values is not taken into account. In this study, by using fractional order accumulation to assign appropriate weights to samples, we propose a fractional grey prediction model with Fourier series that offers high prediction accuracy. Experimental results demonstrate that the proposed grey prediction model performs well compared with other considered prediction models.

Scenario Forecasting for Global Tourism

This study provides innovative forecasts of the probabilities of certain scenarios of tourism demand. The scenarios of interest are constructed in relation to tourism growth and economic growth. The probability forecasts based on these scenarios provide valuable information for destination policy makers. The time-varying parameter panel vector autoregressive (TVP-PVAR) model is adopted for scenario forecasting. Both the accuracy rate and the Brier score are used to evaluate the forecasting performance. A global set of 25 tourism destinations is empirically examined, and the results confirm that the TVP-PVAR model with a time-varying error covariance matrix is generally a promising tool for forecasting. Our study contributes to tourism forecasting literature in advocating the use of scenario forecasting to facilitate industry decision making in situations wherein forecasts are defined by two or more dimensions simultaneously. In addition, it is the first study to introduce the TVP-PVAR model to tourism demand forecasting.

A novel two-step procedure for tourism demand forecasting

ABSTRACT Tourism demand forecasting is a critical process in the planning of tourism utilities. In recent years, Internet search indices have been popularly used as tourism demand indicators. However, due to the complex relationship between tourism demand and the search index and the vast amounts of search engine data, the traditional econometric and artificial intelligence models could not be enough to complete the prediction task. Under this scenario, this paper proposes a novel two-step method to improve tourism demand prediction accuracy. Firstly, a double-boosting algorithm is proposed to select the keywords and their lags from the potential relevant high-dimensional search queries. Second, the ensemble Support Vector Regression (SVR) based Deep belief Network (DBN) approach is adopted to capture the possible non-linear relationship and to improve the forecasting performance through deep learning combination. The empirical results demonstrate that this procedure significantly outperforms other benchmark models when forecasting monthly Hong Kong tourist arrivals.

Hybrid SVR-SARIMA model for tourism forecasting using PROMETHEE II as a selection methodology: a Philippine scenario

Purpose This paper aims to propose a hybrid-forecasting model for long-term tourism demand forecasting. As such, it attempts to model the tourism demand in the Philippines, which is a relatively underrepresented area in the literature, despite its tourism sector’s growing economic progress. Design/methodology/approach A hybrid support vector regression (SVR) – seasonal autoregressive integrated moving averages (SARIMA) model is proposed to model the seasonal, linear and nonlinear components of the tourism demand in a destination country. The paper further proposes the use of multiple criteria decision-making (MCDM) approaches in selecting the best forecasting model among a set of considered models. As such, a preference ranking organization method for enrichment of evaluations (PROMETHEE) II is used to rank the considered forecasting models. Findings The proposed hybrid SVR-SARIMA model is the best performing model among a set of considered models in this paper using performance criteria that evaluate the errors of magnitude, directionality and trend change, of a forecasting model. Moreover, the use of the MCDM approach is found to be a relevant and prospective approach in selecting the best forecasting model among a set of models. Originality/value The novelty of this paper lies in several aspects. First, this paper pioneers the demonstration of the SVR-SARIMA model’s capability in forecasting long-term tourism demand. Second, this paper is the first to have proposed and demonstrated the use of an MCDM approach for performing model selection in forecasting. Finally, this paper is one of the very few papers to provide lenses on the current status of Philippine tourism demand.