Monthly Tourist Arrivals Research Articles

Single or Combine? Tourism Demand Volatility Forecasting with Exponential Weighting and Smooth Transition Combining Methods

Tourism forecasting has garnered considerable interest. However, integrating tourism forecasting with volatility is significantly less typical. This study investigates the performance of both the single models and their combinations for forecasting the volatility of tourism demand. The seasonal autoregressive integrated moving average (SARIMA) model is used to construct the mean equation, and three single models, namely the generalized autoregressive conditional heteroscedasticity (GARCH) family models, the error-trend-seasonal exponential smoothing (ETS-ES) model, and the innovative smooth transition exponential smoothing (STES) model, are employed to estimate the volatility of monthly tourist arrivals into Malaysia. This study also assesses the accuracy of forecasts using simple average (SA), minimum variance (MV), and novel smooth transition (ST). STES performs the best of the single models for forecasting the out-of-sample of tourism demand volatility, followed closely by ETS-ES. In contrast, the ST combining method surpasses SA and MV. Interestingly, forecast combining methods do not always outperform the best single model, but they consistently outperform the worst single model. The MCS and DM tests confirm the aforementioned findings. This article merits consideration for future forecasting research on tourism demand volatility.

Forecasting and modelling for the inbound tourism demand volatility

Many past studies conducted have explored and focused primarily on forecasting and predicting tourism demand. However, this research aims to concentrate on the aspect of how tourism demand volatility can be forecasted and accounted for, rather than solely on how we can forecast and anticipate tourism demand itself. Seven source countries for monthly tourist arrivals in Malaysia will be considered and focused as a case study in this paper. Furthermore, the model theory of generalised autoregressive conditional heteroscedastic (GARCH) family volatility are explored in its relevance and introduced in this study. Additionally, the performance and evaluation of a new adaptive forecasting method, smooth transition exponential smoothing (STES), will be compared to GARCH-type models and several ad hoc methods for Malaysia's tourism demand volatility forecasting and modelling. Notably, our research conducted reported findings that monthly seasonality impacts existed in the mean equation, and the GARCH family models revealed that news shock continuously impact on Malaysia's tourism demand volatility. The EGARCH and GJR models utilised in this study also exhibited the asymmetric and leverage effects. Interestingly, it was also observed that the STES model surpassed GARCH family models and several ad hoc methods in forecasting and modelling.

Intelligent forecasting of inbound tourist arrivals by social networking analysis

Tourism is very important for many countries. Many tourism demand forecasting methodologies are continuously being proposed. Most studies have used lagging economic factors as predictors, but these can cause an inaccurate prediction when unexpected events happen. In this study, a tourism social network will be used in our forecasting model. In addition, a least square support vector regression with genetic algorithm will be developed to predict the monthly tourist arrivals. Grey Relational Analysis indicates that the model outperforms the comparison models, and the null hypothesis of the predicted series having the same mean of the actual series is accepted. The experimental results indicate that the predictors from social network are excellent alternatives to economic indicators.

Forecasting tourist arrivals: Google Trends meets mixed-frequency data

In this article, we examine the usefulness of Google Trends data in predicting monthly tourist arrivals and overnight stays in Prague during the period between January 2010 and December 2016. We offer two contributions. First, we analyze whether Google Trends provides significant forecasting improvements over models without search data. Second, we assess whether a high-frequency variable (weekly Google Trends) is more useful for accurate forecasting than a low-frequency variable (monthly tourist arrivals) using mixed-data sampling (MIDAS). Our results suggest the potential of Google Trends to offer more accurate predictions in the context of tourism: we find that Google Trends information, both 2 months and 1 week ahead of arrivals, is useful for predicting the actual number of tourist arrivals. The MIDAS forecasting model employing weekly Google Trends data outperforms models using monthly Google Trends data and models without Google Trends data.

Spurious patterns in Google Trends data - An analysis of the effects on tourism demand forecasting in Germany

Previous studies show that time series data about the frequency of hits for tourism-related search terms from Google (Google Trends data) is a valuable predictor for short-term tourism demand forecasting in many different tourism regions worldwide. The paper contributes to this literature in three ways. First, it shows that Google Trends data is useful for short-term predictions of monthly tourist arrivals in several German holiday regions. Second, the paper also demonstrates that the Google Trends time series we employ share certain patterns with Google Trends time series used in previous studies, including several studies totally unrelated to the tourism industry. We refer to these artefacts as “spurious patterns” and perform a detailed analysis of their negative impact on forecasting. Last, the paper proposes a method to sanitize Google Trends data and reduce the adverse impact of spurious patterns, thereby paving the way to develop statistically sound tourism demand forecasts.

Combined group method of data handling models using artificial bee colony algorithm in time series forecasting

Time series forecasting is an important area of forecasting which has gained many attentions from various research areas. In line with its popularity, various models have been introduced for the purpose of producing accurate time series forecasts. Nevertheless, it is difficult to find an ideal model as there is no model that can perform best for all types of data. Recently, combination of forecasts has gained immense popularity in the time series forecasting area. Among the well-known combination technique is the weighted-based approach, where appropriate weights are given based on the performance of each individual model. In this paper, a robust methodology for accurate time series forecasting based on Artificial Bee Colony (ABC) algorithm and four Group Method of Data Handling (GMDH) models, namely GMDH-Polynomial, GMDH-Radial Basis Function, GMDH-Sigmoid and GMDH-Tangent are proposed. In this methodology, the first step was to develop forecasts using individual GMDH models. In the second step, weights for each individual GMDH were calculated heuristically using ABC algorithm. In the final step, the results were aggregated to form a new forecast. For the purpose of evaluating the performance of the proposed model, the model was applied to a real time series data which is the monthly tourist arrivals from Singapore to Malaysia from year 2000 to 2017. Based on the empirical results, the application of nonlinear transfer function such as Tangent function has the potential to improve the performance of GMDH model, while the other functions such as Sigmoid function produced a forecast which is worse than the conventional GMDH-Polynomial. However, the combination of GMDH models using ABC managed to outperform all the individual models. The empirical finding demonstrated the efficiency of Combined GMDH model in time series forecasting, as well as the applicability of implementing optimization algorithm to find appropriate weights for the individual models.

Using a Bayesian Structural Time–Series Model to Infer the Causal Impact on Cigarette Sales of Partial and Total Bans on Public Smoking

Abstract The Bayesian structural time series model, used in conjunction with a state–space model, is a novel means of exploring the causal impact of a policy intervention. It extends the widely used difference–in–differences approach to the time series setting and enables several control series to be used to construct the counterfactual. This paper highlights the benefits of using this methodology to estimate the effectiveness of an absolute ban on smoking in public places, compared with a partial ban. In January 2006, the Spanish government enacted a tobacco control law which banned smoking in bars and restaurants, with exceptions depending on the floor space of the premises. In January 2011, further legislation in this area was adopted, removing these exceptions. The data source used for our study was the monthly legal sales of cigarettes in Spain from January 2000 to December 2014. The potential control series were the monthly tourist arrivals from the United Kingdom, the total number of visitors from France, the unemployment rate and the average price of cigarettes. Analysis of the state–space model leads us to conclude that the partial ban was not effective in reducing the tobacco sold in Spain, but that the total ban contributed significantly to reducing cigarette consumption.

Modeling Tourist Arrivals in Sri Lanka

Tourism industry development becomes one of the key strategies for economic development of many countries during the few decades. This study intends to predict monthly tourist arrivals in Sri Lanka for the period of six years from 2015 January to 2020 December using Time series forecasting simulation method. For the development of the model 516 monthly observations are made after adjusted for seasonality using X-13ARIMA-SEATS monthly seasonal adjustment Method. The study uses 36 alternative ARIMA models as simulation to find the best model to predict monthly tourist arrivals in Sri Lanka. The Akaike Information Criterion (AIC) is used as selection criteria for the models which find that the ARIMA (5, 1, 3) as the best model among the alternative models. According to the model, the expected tourist arrivals in the month of December 2020 will be 178, 705 tourists in Sri Lanka.

Tourist displacement in two South African sport mega-events

Crowding-out (or displacement) of non-event visitors has received little attention in the literature on the impact of sports events, largely because it cannot be measured accurately. This paper discusses such effects in conceptual terms and reports the results of an analysis of data on tourist arrivals in South Africa aimed at estimating the displacement effects of two sports events held in 2009: the Indian Premier League cricket tournament and the British and Irish Lions rugby tour. Using monthly tourist arrivals in South Africa from specific countries, we find that some tourists from countries not participating in these events were displaced; the much stronger effect, however, was that tourists from the participating countries re-arranged their visits to coincide with an event. While confirming the inherent difficulty of measuring crowding-out effects, this paper shows that characteristics of events can sometimes be exploited to obtain useful information on displacement from readily available data.

Modelling the domestic and foreign tourist arrivals to Tamil Nadu (India)

In recent years, due to the Indian Federal Government's and Tamil Nadu State Government's various initiatives and promotional activities, foreign and domestic tourist arrivals in Tamil Nadu are on the increase. This article aims to model the monthly tourist arrivals (foreign as well as domestic) in Tamil Nadu for monthly time series data during the period 1998 to 2002 using univariate time-series models. As both time series show strong seasonal patterns, we also investigate the possibility of seasonal unit roots in the domestic and foreign tourist arrivals series. The results show that significant growth in domestic and foreign arrivals takes place during the months December to January. Growth rate for domestic tourist arrivals is positive during April and May, but is negative for the foreign tourist arrivals during April and insignificant during May. Such information would be very useful to the Tamil Nadu government and the tourism industry in maximizing the usage of available tourist spot infrastructure and to provide high quality service.

Forecasting tourist arrivals by using the adaptive network-based fuzzy inference system

Since accurate forecasting of tourist arrivals is very important for planning for potential tourism demand and improving the tourism infrastructure, various tourist arrivals forecasting methods have been developed. The purpose of this study is to apply the adaptive network-based fuzzy inference system (ANFIS) model to forecast the tourist arrivals to Taiwan and demonstrate the forecasting performance of this model. Based on the mean absolute percentage errors and statistical results, we can see that the ANFIS model has better forecasting performance than the fuzzy time series model, grey forecasting model and Markov residual modified model. Thus, the ANFIS model is a promising alternative for forecasting the tourist arrivals. We also use the ANFIS model to forecast the monthly tourist arrivals to Taiwan from Japan, Hong Kong and Macao, and the United States.

Effects of News Shock on Inbound Tourist Demand Volatility in Korea

This study introduces the concepts and theories relating to conditional heteroscedastic volatility models and the news impact curve, and applies them in the analysis of the tourist market in Korea. Three volatility models are used to estimate the conditional volatility of monthly arrivals of inbound tourists into Korea, and news impact curves are examined in the context of these models. The major findings of this study include the existence of monthly seasonality in conditional mean equations, the existence of asymmetric effects from the EGARCH and TARCH models, and the persistence of the impact of news shock on monthly tourist arrivals into Korea in the estimation of the GARCH model.

Monthly seasonal variations: Asian tourism to Australia

Tourist arrivals series from Hong Kong, Malaysia, and Singapore to Australia exhibit strong seasonality. For data and policy analysis, it is useful to obtain seasonally adjusted data for international tourism from the respective origin countries. This paper applies the moving average technique for estimating the seasonal components of time series to monthly tourist arrivals time series data to Australia. The autocorrelation and partial autocorrelation functions, the Lagrange multiplier test for the absence of serial correlation, and model selection criteria, namely the Akaike Information Criterion and Schwarz Bayesian Criterion, are used to examine which time series processes best describe international arrivals data for Australia.