Time Series Forecasting Techniques: Short‐term Planning in
Tourism

Athiyaman, Adee; Robertson, R. W.

doi:10.1108/09596119210018864

Cited by 50 publications

(28 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Examples are regression and time-series forecasting for short-term demand planning in tourism [14], time-series for pricing of financial assets [15] in financial markets, multiple linear regressions for cash-flow forecasting in the construction industry [16], or dynamic regression models for cost forecasting in the construction industry [17].…”

Section: Business Performance Managementmentioning

confidence: 99%

Analytical support for predicting cost in complex service delivery environments

Vogt¹,

Mattfeldt²,

Satzger³

et al. 2014

IBM J. Res. & Dev.

View full text Add to dashboard Cite

Section: Business Performance Managementmentioning

confidence: 99%

Analytical support for predicting cost in complex service delivery environments

Vogt¹,

Mattfeldt²,

Satzger³

et al. 2014

IBM J. Res. & Dev.

View full text Add to dashboard Cite

“…Time series models have been widely used for tourism demand forecasting (Song and Li, 2008). They are able to generate valuable results in tourism and hospitality forecasts (Athiyaman and Robertson, 1992;Witt and Witt, 1995). In this study, simple ARIMA modeling is used to determine the forecasting model on the room rates of three-star hotels in Macao.…”

Section: Literature Reviewmentioning

confidence: 99%

An attempt of a crystal ball for hotels in a gaming destination, Macao

Lam

Lei

2015

International Journal of Tourism Cities

View full text Add to dashboard Cite

Access to this document was granted through an Emerald subscription provided by emerald-srm:451335 [] For AuthorsIf you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The purpose of this paper is to consider the issue of forecasting hotel room rate with data from 2004 onwards and the forecast horizons of 91 observations. Design/methodology/approach -This study employs a set of time series data (91 observations) on average monthly hotel room rates to generate an Autoregressive Integrated Moving Average Models (ARIMA) forecasting model. Findings -Through the employment of 74 percent observations, with 26 percent withhold for evaluation checking, six best models are identified from 50 models under study. The final model reports a high level of predictive accuracy and provides useful insights for hoteliers to form business strategies. Originality/value -This research provides a well-defined model to forecast the room rate of three-star hotels in the city. The research findings provide good ground for strategic management of the industry, in which there is an imbalance between demand and supply of hotel accommodations. In addition, being the first of its kind in one of the largest gaming revenue generation city in the world, this research provides valuable information for further research of its kind in the future.

show abstract

“…The natural advantages of a time series forecasting model are that the model is simple to apply and requires no more than a data series. Researchers claimed time series forecasting models are able to produce accurate results in hospitality and tourism forecasts (Athiyaman and Robertson, 1992;Witt and Witt, 1995). In particular, Andrew et al (1990) examined the forecasting accuracy of monthly occupancy rates for a major center-city hotel using two time series forecasting models.…”

Section: Introductionmentioning

confidence: 99%

Initially Testing an Improved Extrapolative Hotel Room Occupancy Rate Forecasting Technique

Law

2004

Journal of Travel & Tourism Marketing

View full text Add to dashboard Cite

The existing time series forecasting models either capture the information of 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.

show abstract

Time Series Forecasting Techniques: Short‐term Planning in Tourism

Cited by 50 publications

References 9 publications

Analytical support for predicting cost in complex service delivery environments

Analytical support for predicting cost in complex service delivery environments

An attempt of a crystal ball for hotels in a gaming destination, Macao

Initially Testing an Improved Extrapolative Hotel Room Occupancy Rate Forecasting Technique

Contact Info

Product

Resources

About