Modeling Seasonality in Tourism Forecasting

doi:10.1177/0047287505276605

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Modeling Seasonality in Tourism Forecasting

Journal Article•DOI•

Modeling Seasonality in Tourism Forecasting

Nada Kulendran¹, Kevin K. F. Wong²•Institutions (2)

Victoria University, Australia¹, Hong Kong Polytechnic University²

01 Nov 2005-Journal of Travel Research (SAGE Publications)-Vol. 44, Iss: 2, pp 163-170

TL;DR: In this paper, two forecasting models, RIMA14 and ARIMA1, are used for modeling stochastic nonstationary seasonality and requires first and fourth differences to achieve stationarity.

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Abstract: Within the multiplicative seasonal ARIMA modeling context, there are two forecasting models, RIMA14 and ARIMA1. ARIMA14 is used for modeling stochastic nonstationary seasonality and requires first and fourth differences to achieve stationarity. ARIMA1 considers the series only in first differences, and seasonality is modeled with a constant and three seasonal dummies. The selection of either model depends on the nature of seasonality. Conventional unit root tests determine the nature of seasonality and the order of integration and, therefore, the series' choice of forecasting model. To determine whether the test correctly identifies the forecasting model for tourism demand, out-of-sample forecasting performance of ARIMA1 and ARIMA14 is compared with HEGY unit root model selection method. Comparing forecasting performance of both models with HEGY unit root model selection shows that the outcome of HEGY test procedure may not be useful in the selection of a univariate time-series model for quarterly tourism demand series.

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Journal Article•DOI•

Tourism Demand Modelling and Forecasting: A Review of Recent Research

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Haiyan Song¹, Gang Li²•Institutions (2)

Hong Kong Polytechnic University¹, University of Surrey²

01 Apr 2008-Tourism Management

TL;DR: A review of the published studies on tourism demand modelling and forecasting since 2000 is presented in this article, where the authors identify some new research directions, which include improving the forecasting accuracy through forecast combination; integrating both qualitative and quantitative forecasting approaches, tourism cycles and seasonality analysis, events' impact assessment and risk forecasting.

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977 citations

Journal Article•DOI•

A review of research on tourism demand forecasting: Launching the Annals of Tourism Research Curated Collection on tourism demand forecasting

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Haiyan Song¹, Richard T.R. Qiu¹, Jinah Park¹•Institutions (1)

Hong Kong Polytechnic University¹

01 Mar 2019-Annals of Tourism Research

TL;DR: This paper reviewed 211 key papers published between 1968 and 2018 for a better understanding of how the methods of tourism demand forecasting have evolved over time, and found that forecasting models have grown more diversified, that these models have been combined, and that the accuracy of forecasting has been improved.

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263 citations

Journal Article•DOI•

A meta-analysis of international tourism demand forecasting and implications for practice

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Bo Peng¹, Haiyan Song², Geoffrey I. Crouch³•Institutions (3)

City University London¹, Hong Kong Polytechnic University², La Trobe University³

01 Dec 2014-Tourism Management

TL;DR: This article used meta-analysis to examine the relationship between the accuracy of different forecasting models, and the data characteristics and study features, and found that the origins of tourists, destination, time period, modeling method, data frequency, number of variables and their measures and sample size all significantly influence the forecasting models.

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200 citations

Journal Article•DOI•

Forecasting Destination Weekly Hotel Occupancy with Big Data

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Bing Pan¹, Bing Pan², Yang Yang³•Institutions (3)

Shaanxi Normal University¹, Pennsylvania State University², Temple University³

01 Sep 2017-Journal of Travel Research

TL;DR: Time-series models incorporating several tourism big data sources, including search engine queries, website traffic, and weekly weather information, are tested in order to construct an accurate forecasting model of weekly hotel occupancy for a destination and show the superiority of ARMAX models with both search search queries and website traffic data in accurate forecasting.

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Abstract: Hospitality constituencies need accurate forecasting of future performance of hotels in specific destinations to benchmark their properties and better optimize operations. As competition increases, hotel managers have urgent need for accurate short-term forecasts. In this study, time-series models incorporating several tourism big data sources, including search engine queries, website traffic, and weekly weather information, are tested in order to construct an accurate forecasting model of weekly hotel occupancy for a destination. The results show the superiority of ARMAX models with both search engine queries and website traffic data in accurate forecasting. Also, the results suggest that weekly dummies are superior to Fourier terms in capturing the hotel seasonality. The limitations of the inclusion of multiple big data sources are noted since the reduction in forecasting error is minimal.

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152 citations

Cites background or methods from "Modeling Seasonality in Tourism For..."

...ARIMA models incorporate the autoregressive and moving average parts of stationary data (Kulendran and Wong 2005); BSM and STSM models analyze time series by estimating different components (Cortés-Jiménez and Blake 2011; Kulendran and Wong 2011); GARCH models capture the conditional variance…...
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...Even though some statistical tests can be used to select between deterministic and stochastic methods in modeling seasonality, Kulendran and Wong (2005) showed that these tests may yield misleading results after evaluating the forecasting performance of different models....
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...Seasonality can be incorporated in the time series model in two ways, by either a stochastic method or a deterministic method (Kulendran and Wong 2005)....
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...To treat seasonality as a stochastic component, the data can be seasonally differenced (Kulendran and Wong 2005) or modeled using a state space form with a seasonal component (Song et al. 2011)....
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Journal Article•DOI•

Spatial Distribution of Tourist Flows to China's Cities

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Yang Yang¹, Kevin K. F. Wong²•Institutions (2)

University of Florida¹, Hong Kong Polytechnic University²

09 May 2013-Tourism Geographies

TL;DR: Wang et al. as discussed by the authors investigated the spatial distribution of inbound and domestic tourist flows to cities in China and their growth rates using exploratory spatial data analysis, and they showed that tourism flows are polarized into clusters and remain very stable over time.

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Abstract: This paper investigates the spatial distribution of inbound and domestic tourist flows to cities in China and their growth rates using exploratory spatial data analysis. This method is a set of GIS spatial statistical techniques that are useful in describing and visualizing the spatial distribution, detecting patterns of hot-spots, and suggesting spatial regimes. The global Moran's I statistics for inbound and domestic tourist flows reveal strong positive and significant spatial autocorrelation. Furthermore, the Moran significance maps indicate four significant inbound tourism hot-spot areas in 1999 and 2006 (the Beijing-Tianjin cluster, the Yangtze River Delta cluster, the Fujian coast cluster and the Pearl River Delta cluster), and five significant domestic tourism hot-spot areas in 2002 and 2006 (with the addition of the Chengdu cluster). Based on the results, we show that tourism flows are polarized into clusters and remain very stable over time. As has been seen in other countries, hot-spots...

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116 citations

Cites background from "Modeling Seasonality in Tourism For..."

...Since seasonal fluctuation is common in tourist flows (Kulendran & Wong 2005), the hot-spot areas of tourist flows tend to be distinct across different seasons....
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Journal Article•DOI•

Time Series Analysis: Forecasting and Control

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Michael D. Geurts, George E. P. Box, Gwilym M. Jenkins

01 May 1977-Journal of Marketing Research

TL;DR: Time Series Analysis and Forecasting: principles and practice as mentioned in this paper The Oxford Handbook of Quantitative Methods, Vol. 3, No. 2: Statistical AnalysisTime-Series ForecastingPractical Time-Series AnalysisApplied Bayesian Forecasting and Time Series AnalysisSAS for Forecasting Time SeriesApplied Time Series analysisTime Series analysisElements of Nonlinear Time Series analyses and forecastingTime series analysis and forecasting by Example.

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Abstract: Advances in Time Series Analysis and ForecastingThe Analysis of Time SeriesForecasting: principles and practiceIntroduction to Time Series Analysis and ForecastingThe Oxford Handbook of Quantitative Methods, Vol. 2: Statistical AnalysisTime-Series ForecastingPractical Time Series AnalysisApplied Bayesian Forecasting and Time Series AnalysisSAS for Forecasting Time SeriesApplied Time Series AnalysisTime Series AnalysisElements of Nonlinear Time Series Analysis and ForecastingTime Series Analysis and Forecasting by ExampleIntroduction to Time Series Analysis and ForecastingTime Series Analysis and AdjustmentSpatial Time SeriesPractical Time Series Forecasting with RA Very British AffairMachine Learning for Time Series Forecasting with PythonTime Series with PythonTime Series Analysis: Forecasting & Control, 3/EIntroduction to Time Series Forecasting With PythonThe Analysis of Time SeriesTime Series Analysis and Its ApplicationsForecasting and Time Series AnalysisIntroduction to Time Series and ForecastingIntroduction to Time Series Analysis and ForecastingTime Series Analysis in the Social SciencesPractical Time Series AnalysisTime Series Analysis and ForecastingTheory and Applications of Time Series AnalysisApplied Time SeriesSAS for Forecasting Time Series, Third EditionTime Series AnalysisPredictive Modeling Applications in Actuarial ScienceIntroductory Time Series with RHands-On Time Series Analysis with RAdvances in Time Series ForecastingTime Series Analysis and Forecasting Using Python & RAdvanced Time Series Data Analysis

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6,184 citations

Posted Content•

Tests for Unit Roots: a Monte Carlo Investigation

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G. William Schwert¹, G. William Schwert²•Institutions (2)

University of Rochester¹, National Bureau of Economic Research²

01 Dec 1988-Social Science Research Network

TL;DR: In particular, the tests developed by Phillips and Perron (1988) seem more sensitive to model misspeciflcation than the high order autoregressive approximation suggested by Said and Diekey(1984) as mentioned in this paper.

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Abstract: Recent work by Said and Dickey (1984 ,1985) , Phillips (1987), and Phillips and Perron(1988) examines tests for unit roots in the autoregressive part of mixed autoregressive-integrated-moving average (ARIHA) models (tests for stationarity). Monte Carlo experiments show that these unit root tests have different finite sample distributions than the unit root tests developed by Fuller(1976) and Dickey and Fuller (1979, l981) for autoregressive processes. In particular, the tests developed by Philllps (1987) and Phillips and Perron (1988) seem more sensitive to model misspeciflcation than the high order autoregressive approximation suggested by Said and Diekey(1984).

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1,495 citations

"Modeling Seasonality in Tourism For..." refers background in this paper

...Unit root tests may lack power (Schwert 1989) and, therefore, favor the null hypothesis, which may lead to the selection of the incorrect ARIMA(1) or ARIMA(14) model....
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...Unit root tests may lack power (Schwert 1989) and, therefore, favor the null hypothesis, which may lead to the selection of the incorrect ARIMA1 or ARIMA14 model....
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Journal Article•DOI•

Seasonal integration and cointegration

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Svend Hylleberg¹, Robert F. Engle², Clive W. J. Granger², Byung Sam Yoo³•Institutions (3)

Aarhus University¹, University of California, San Diego², Pennsylvania State University³

01 Apr 1990-Journal of Econometrics

TL;DR: The authors developed tests for roots in linear time series which have a modulus of one but which correspond to seasonal frequencies and used them to examine cointegration at different frequencies between consumption and income in the U.K.

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1,489 citations

"Modeling Seasonality in Tourism For..." refers background or methods in this paper

...The order of integration based on the HEGY unit root test (Hylleberg et al. 1990) results presented in Tables 2 and 3 shows which of the ARIMA1 and ARIMA14 models should be selected for forecasting Australia inbound VFR, holiday, business, and total tourism, and UK outbound VFR, holiday, business,…...
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...When there is change in the nature of seasonal variation in the out-of-sample period compared to the estimation period, the HEGY unit root test (Hylleberg et al. 1990) may not select the best forecasting model out of ARIMA1 and ARIMA14....
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...Kulendran and King (1997) used the HEGY unit root tests (Hylleberg et al. 1990) to determine the order of integration of quarterly international tourism demand to Australia....
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...To determine the order of integration and to test for a unit root at zero frequency and seasonal frequencies, unit root tests such as the Augmented-Dickey Fuller test (Goh and Law 2002) and the HEGY (Hylleberg et al. 1990) test (Kulendran and King 1997; Kulendran and Witt 2001) were used....
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...The HEGY test (Hylleberg et al. 1990) was used in tourism demand studies to test for unit roots at both biannual and annual frequencies, as well as the usual zero frequency....
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Report•DOI•

Tests for Unit Roots: A Monte Carlo Investigation

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G. William Schwert

01 Apr 1989-Journal of Business & Economic Statistics

TL;DR: In particular, the tests developed by Phillips and Perron as mentioned in this paper seem more sensitive to model misspecification than the high-order autoregressive approximation suggested by Said and Dickey (1984, 1985), Phillips (1987), and Phillips and perron (1988).

...read moreread less

Abstract: Recent work by Said and Dickey (1984, 1985), Phillips (1987), and Phillips and Perron (1988) examines tests for unit roots in the autoregressive part of mixed autoregressive integrated moving average models (tests for stationary). Monte Carlo experiments show that these unit-root tests have different finite-sample distributions from the unit-root tests developed by Fuller (1976) and Dickey and Fuller (1979, 1981) for autoregressive processes. In particular, the tests developed by Phillips (1987) and Phillips and Perron (in press) seem more sensitive to model misspecification than the high-order autoregressive approximation suggested by Said and Dickey (1984).

...read moreread less

1,250 citations

Journal Article•DOI•

Forecasting tourism demand: A review of empirical research

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Stephen F. Witt¹, Christine A. Witt²•Institutions (2)

Swansea University¹, University of Bradford²

01 Sep 1995-International Journal of Forecasting

TL;DR: In this paper, the main methods used to forecast tourism demand which are reported in published empirical studies are discussed, together with the empirical findings, and the most appropriate explanatory variables are examined.

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891 citations

"Modeling Seasonality in Tourism For..." refers methods in this paper

...The Witt and Witt (1995) and Witt, Song, and Within the multiplicative seasonal ARIMA modeling context, there are two forecasting models, ARIMA14 and ARIMA1....
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