酒店客房入住率预测方法的比较

N. M. Desa, Muzhaffar Bin Mohamad Marzuki
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引用次数: 1

摘要

有几种类型的预测类别已经被使用,如酒店房间入住率预测。这类预测的实施是至关重要的,因为它可以为所有酒店部门提供有效的计划和决策。因此,本研究旨在比较最佳的酒店客房入住率预测方法。因此,为了确定哪一种预测方法最适合使用2012年至2017年的二手数据预测酒店客房入住率,将实施季节性天真,季节性霍尔特冬季方法和ARIMA。最佳方法的选择基于三种误差测量,即均方根误差(RMSE)、平均绝对百分比误差(MAPE)和平均绝对误差(MAE)。从所进行的分析中,结果表明最好的方法是季节性霍尔特冬季乘法法,因为它显示了所有三种测量的最低误差。此外,对2018年未来酒店客房入住率的预测显示出与往年相似的模式。2018年未来入住率与2017年实际入住率比较,各月份酒店客房入住率均有增减。有几种类型的预测类别已经被使用,如酒店房间入住率预测。这类预测的实施是至关重要的,因为它可以为所有酒店部门提供有效的计划和决策。因此,本研究旨在比较最佳的酒店客房入住率预测方法。因此,为了确定哪一种预测方法最适合使用2012年至2017年的二手数据预测酒店客房入住率,将实施季节性天真,季节性霍尔特冬季方法和ARIMA。最佳方法的选择基于三种误差测量,即均方根误差(RMSE)、平均绝对百分比误差(MAPE)和平均绝对误差(MAE)。从所进行的分析中,结果表明最好的方法是季节性霍尔特冬季乘法法,因为它显示了所有三种测量的最低误差。此外,对未来酒店客房入住率的预测…
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A comparison of forecasting methods for hotel room occupancy
There are a few types of forecasting categories that have been used such as hotel room occupancy forecast. Implementation of this forecasting category can be crucial because it leads to an efficient planning for, and decision making to all the hotel departments. Thus, this study aims to compare the best forecasting method for hotel room occupancy. Therefore, Seasonal Naive, Seasonal Holt Winter’s Method and ARIMA are going to be implemented in order to determine which forecasting method is most suitable to forecast hotel room occupancy by using secondary data from year 2012 until 2017. The selection of best method is based on three error measurements which are root mean square error (RMSE), mean absolute percentage error (MAPE) and mean absolute error (MAE). From the analysis conducted, the results show the best method to be implemented is the Seasonal Holt Winter’s Multiplicative method since it shows the lowest error for all three measurements. Furthermore, the forecast of future hotel room occupancy for year 2018 shows similar pattern as previous years. In comparing 2018 future occupancy with 2017 actual occupancy, there are some increment and decrement in hotel room occupancy for various months.There are a few types of forecasting categories that have been used such as hotel room occupancy forecast. Implementation of this forecasting category can be crucial because it leads to an efficient planning for, and decision making to all the hotel departments. Thus, this study aims to compare the best forecasting method for hotel room occupancy. Therefore, Seasonal Naive, Seasonal Holt Winter’s Method and ARIMA are going to be implemented in order to determine which forecasting method is most suitable to forecast hotel room occupancy by using secondary data from year 2012 until 2017. The selection of best method is based on three error measurements which are root mean square error (RMSE), mean absolute percentage error (MAPE) and mean absolute error (MAE). From the analysis conducted, the results show the best method to be implemented is the Seasonal Holt Winter’s Multiplicative method since it shows the lowest error for all three measurements. Furthermore, the forecast of future hotel room occupancy fo...
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