Initially Testing an Improved Extrapolative Hotel Room Occupancy Rate Forecasting Technique

Law, Rob

doi:10.1300/j073v16n02_06

Cited by 36 publications

(22 citation statements)

References 8 publications

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“…Less frequently applied is the exponential smoothing class of time series models, despite evidence that such models often furnish adequate forecasts of directional and trend changes in tourism demand (Cho, 2003;Witt, Newbould, & Watkins, 1992). Naïve 1 (no change) and Naïve 2 (constant growth) models fall into the time series category and are frequently used as benchmarks for assessing predictive accuracy in tourism forecasting exercises (Hu, Chen, & McCain, 2004;Kulendran & Witt, 2003;Law, 2004;Oh & Morzuch, 2005).…”

Section: Introductionmentioning

confidence: 99%

A management orientated approach to combination forecasting of tourism demand

Coshall

Charlesworth

2011

Tourism Management

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Section: Introductionmentioning

confidence: 99%

A management orientated approach to combination forecasting of tourism demand

Coshall

Charlesworth

2011

Tourism Management

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“…Previous literature reveals the importance of forecasting the direction of change of a data series instead of the actual magnitude (Witt and Witt, 1995). More recently, Law (2004) studied extrapolative time series forecasting technique in computing future hotel occupancy rates and made an initial testing on a forecasting technique -Improved Extrapolative Room Occupancy Rate Forecasting Model and demonstrated the unique feature of the model that it uses an incremental approach to calculate the growth rate in the last trend of the data series. It combats the disadvantage of time-series model in which it captures either the information of a few numbers towards the ending period of the data series or the entire data series is used for projecting future values.…”

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

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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.

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“…Traditional tourism demand researches tend to use linear parametric time series forecasting models. The most popular are the autoregressive integrated moving average models [3]- [5], the naive method [6], [7], and the exponential smoothing model [8]. However, the predictions obtained using these traditional models are usually imprecise, and it is difficult to utilize these models to approximate nonlinear and irregular tourism time series [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Using a Single Dendritic Neuron to Forecast Tourist Arrivals to Japan

Chen

Sun

Gao

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYWith the fast growth of the international tourism industry, it has been a challenge to forecast the tourism demand in the international tourism market. Traditional forecasting methods usually suffer from the prediction accuracy problem due to the high volatility, irregular movements and non-stationarity of the tourist time series. In this study, a novel single dendritic neuron model (SDNM) is proposed to perform the tourism demand forecasting. First, we use a phase space reconstruction to analyze the characteristics of the tourism and reconstruct the time series into proper phase space points. Then, the maximum Lyapunov exponent is employed to identify the chaotic properties of time series which is used to determine the limit of prediction. Finally, we use SDNM to make a short-term prediction. Experimental results of the forecasting of the monthly foreign tourist arrivals to Japan indicate that the proposed SDNM is more efficient and accurate than other neural networks including the multi-layered perceptron, the neuro-fuzzy inference system, the Elman network, and the single multiplicative neuron model.

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Initially Testing an Improved Extrapolative Hotel Room Occupancy Rate Forecasting Technique

Cited by 36 publications

References 8 publications

A management orientated approach to combination forecasting of tourism demand

A management orientated approach to combination forecasting of tourism demand

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

Using a Single Dendritic Neuron to Forecast Tourist Arrivals to Japan

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