Developing a Forecast System for Hotel Occupancy Rate Using Integrated ARIMA Models

Abstract

International tourist hotels play a consequential role in the development of the tourism industry. The occupancy rate is usually considered a pertinent indicator in measuring the performance of the hotels. This study employs the ARIMA and ARIMA transfer function model incorporated with the Box-Cox transformation function for the forecasting of occupancy rate. The results of this research find two explanatory variables strongly affect the occupancy rate: one is the numbers of tourists and the other is the Taiwan/ Japan foreign exchange rate. The forecasting shows slow rising of the occupancy rate for the international hotels in Taiwan; it will reach 64.67% by the year 2000. The forecast of the occupancy rate provides important information tor both government agencies and hotel managers so that corresponding management strategies can be made.     

Data Mining For Hotel Occupancy Rate: An Independent Component Analysis Approach

The recent global financial crisis and the threat of a worldwide H1N1 influenza epidemic have greatly affected the tourism and hospitality industries around the world. Both hospitality practitioners and researchers are interested in finding analytical methods that enable forecasts to be made of hotel room demand under the uncertain conditions likely to affect the industry. In this article, a novel data mining technique called independent component analysis (ICA) is proposed to establish the major factors determining the hotel occupancy rate in Hong Kong. Then, extension of the model is suggested, incorporating these factors to decompose hotel occupancy rates and examine the effect of each factor on the hotel occupancy rate. Empirical findings show that outbreaks of infectious diseases, economic performance, and service price were the major determinants of the hotel occupancy rate in Hong Kong over the period studied.     

Forecasting tourism revisited

This article examines the accuracy and efficiency of forecasting techniques by applying time series regression to forecasting visitor arrivals. Past studies have shown that simpler time series techniques perform as well or better than complex forecasting models. An assessment of visitor forecasts developed at regional, destination and individual market levels suggests that time series regression performs well in producing annual forecasts of visitors which can also serve as a baseline for evaluating the net returns from applying more complex techniques. Tourism managers should appreciate the usefulness of simpler formal methods in developing forecasts of visitors.     

Forecasting Demand for Australian Passports

We forecast demand for Australian passports using a number of univariate and multivariate forecasting models, and assess their relative predictive ability over a number of forecasting horizons and evaluation measures. Our key result is to use different forecasting models for predicting passport demand in the short- versus medium- to long-run. Specifically, to forecast Adult-and-Senior passport demand in the short-term (i.e. up to 12 months) univariate ARIMA models are preferred, while for the longer term forecasts multivariate models with exogenous variables outperform, although only marginally. To forecast passport demand for Minors (less than 18 years old) ARIMA models perform well both in the short-term and the long-term, although ARIMA with explanatory variables outperforms slightly.     

Forecasting in Short-Term Planning and Management for a Casino Buffet Restaurant

SUMMARY

Demand fluctuation accounts for an important consideration in a restaurant's daily operational decisions. Good short-term planning and management require accurate forecasts of daily demand. The objective of this study is three-fold: (1) to apply, evaluate, and compare different methods of forecasting customer counts for an on-premises buffet restaurant of a local casino in Las Vegas, (2) to describe and propose a combined forecasting approach for this casino buffet restaurant, and (3) to explore the concept of revenue and capacity management for this buffet restaurant. Eight forecasting models were tested and evaluated by two common error measures. The results suggest that a double moving average model was the most accurate model with the smallest MAPE and RMSPE. Extensive discussions on forecasting and planning/management in buffet operations are provided along with recommended future research.