Using machine learning and big data for efficient forecasting of hotel booking cancellations

Cancellations are a key aspect of hotel revenue management because of their impact on room reservation systems. In fact, very little is known about the reasons that lead customers to cancel, or how it can be avoided. The aim of this paper is to propose a means of enabling the forecasting of hotel booking cancellations using only 13 independent variables, a reduced number in comparison with related research in the area, which in addition coincide with those that are most often requested by customers when they place a reservation. For this matter, machine-learning techniques, among other artificial neural networks optimised with genetic algorithms were applied achieving a cancellation rate of up to 98%. The proposed methodology allows us not only to know about cancellation rates, but also to identify which customer is likely to cancel. This approach would mean organisations could strengthen their action protocols regarding tourist arrivals.     

The non-linear impact of oil price on the oil demand

ABSTRACT

A panel smooth transition regression model was adopted to analyse the non-linear impact of oil prices on oil demand. Data for 42 countries was obtained from the International Energy Agency for the time period spanning from January 1990 to June 2017. The results indicate that a threshold value does exist. Furthermore, when the oil price was lower than this threshold value, a positive relationship between oil price and oil demand was observed. When the price of oil was higher than the threshold value, however, a negative relationship between price and demand was found.     

Determinants of revenue per available room: Influential roles of average daily rate,demand, seasonality and yearly trend

Determining price per room to be charged to customers is an important decision to be taken by hotel management. Hotels frequently change their room rates based on the demand of room, occupancy rate, seasonal pattern, and strategies undertaken by other hotels on pricing. We formulated four models to analyse how various influencing variables, such as hotel price, demand, yearly trend and monthly seasonality influence hotel revenue per available room (RevPar). To analyse a case, we used monthly accommodation statistics for Sweden taken for Swedish Agency for Economic and Regional Growth and Statistics from January 2008 to July 2017. We carried out data analysis using both multiple regression and Multivariate Adaptive Regression Splines (MARS) model and found that application of MARS can help establishing a nonlinear relationship of RevPar with other determining variables in a superior way. We also proposed the possibility of developing a better forecasting model using MARS.     

Did foreign exchange holding influence growth performance during the global financial crisis?

In a context of increased foreign exchange reserves holding from emerging and developing countries, this paper investigates the diminishing return of reserves holding assumption over the most severe phase of the global financial crisis (2008Q1–2010Q4). Relying on a Panel Smooth Transition Regression model, we highlight the differential effect of the accumulation of foreign exchange reserves for a set of financial vulnerabilities variables. In a specific manner, although reserves accumulation is effective above a critical threshold to cope with vulnerabilities related to the financial channel, we show that it becomes less effective beyond a certain threshold for domestic bank vulnerabilities. Our results are robust to alternative specifications.     

Semiparametric time series models driven by latent factor

We introduce a class of semiparametric time series models (SemiParTS) driven by a latent factor process. The proposed SemiParTS class is flexible because, given the latent process, only the conditional mean and variance of the time series are specified. These are the primary features of SemiParTS: (i) no parametric form is assumed for the conditional distribution of the time series given the latent process; (ii) it is suitable for a wide range of data: non-negative, count, bounded, binary, and real-valued time series; (iii) it does not constrain the dispersion parameter to be known. The quasi-likelihood inference is employed in order to estimate the parameters in the mean function. Here, we derive explicit expressions for the marginal moments and for the autocorrelation function of the time series process so that a method of moments can be employed to estimate the dispersion parameter and also the parameters related to the latent process. Simulated results that aim to check the proposed estimation procedure are presented. Forecasting procedures are proposed and evaluated in simulated and real data. Analyses of the number of admissions in a hospital due to asthma and a total insolation time series illustrate the potential for practical situations that involve the proposed models.     

Determining the predictors for ease of sleep while on aircraft: Regression and qualitative analyses

BackgroundFactors predicting passengers’ ability to fall asleep and levels of sleep anxiety, while traveling on a commercial flight, are investigated through a two-study mixed design.MethodsData collected from approximately 400 participants contributed to the development and validation of multiple regression equations and model fit analysis; and participants responded to related open-ended questions.ResultsRegression equations yielded between two to seven predictors and predicted between 6.7% and 27.7% of the variance, ps < .001. Model fit was strong in all cases. An inductive qualitative approach provided detailed insight into passengers’ concerns and barriers over sleeping on a commercial flight.DiscussionAs the field of commercial aviation continues growing, researching and understanding passengers' experiences and perceptions is crucial to the success of the industry as consumers ultimately drive the market. Passengers’ perceptions of sleep quality on commercial aircraft is a key factor influencing their traveling decision. Therefore, a better understanding of this phenomenon can provide crucial information to future passengers, airline companies, regulatory agencies, and manufacturers, potentially influencing the future success of the aviation industry.     

Modelling demand for lotto using a novel method of correcting for endogeneity

Modelling lottery sales as a function of the mean, standard deviation and skewness of the probability distribution of returns potentially gives insights into how the design of a game could be modified to maximise net revenue. But use of OLS is problematic because the level of sales itself affects values of the moments (and insufficient instruments are available for IV regression). We draw on the concept of a rational expectations equilibrium, developing a new regression model which corrects for endogeneity where the causal impact of the dependent variable on the right-hand side variables is deterministic. We apply the model to data on lotto sales from Spain. Using the Spanish data, we show that results provide more reliable guidance to lottery agencies because accounting for endogeneity leads to significantly different results from OLS and these results have superior performance in out-of-sample forecasting of sales. More generally, results prove consistent with the Friedman-Savage explanation of why people buy lottery tickets and with evidence from racetrack data that ‘bettors love skewness’.     

公共资本和私人资本的生产效率及其区域差异——基于分位数回归模型的研究

本文基于中国各省区市1994～2005年的样本数据,利用分位数回归估计了各区域和各省区市的公共资本和私人资本在各分位点的产出弹性,主要结论是:私人资本的产出弹性远远大于公共资本,私人资本的产出弹性系数均为正,且基本上显著;而对于公共资本,除少数省区市外,东中部各省区市的产出弹性系数为正,而西部省区市的弹性系数则基本上为负,且大多数并不显著。各省区市的公共资本和私人资本不仅在产出弹性大小上存在较大差异,而且在条件分布的不同分位点,其弹性的变化规律也不尽相同。     

Is more IT offshoring better?: An exploratory study of western companies offshoring to South East Asia

We explore the link between a company's performance and the extent of its offshoring of IT-enabled services, focusing on large western companies. Our performance measures comprise sales, profit as percentage of sales, profit/employee and sales/employee over 1999–2004. To measure offshoring, we consider the extent to which these companies have offshored: (1) software development and other IT-related development and maintenance, (2) business processes such as payroll or claims processing, and (3) call centers. We performed cluster analysis using the three corresponding offshoring variables to obtain broad patterns of offshoring. Then we compared the average performance of the companies in different clusters using ANOVA; did a regression analysis of the performance measures against the three offshoring variables; and performed non-parametric correlations within industry sectors. None of these tests indicated any clear link between company performance and the extent of offshoring thus suggesting that further study is needed to understand when to offshore and how best to do it.     

基于互联网大数据的CPI舆情指数构建与应用

研究目标：基于互联网大数据构建CPI舆情指数辅助预测CPI。研究方法：提出了一种构建CPI低频与高频舆情指数的统计方法,并通过选用2006年6月至2015年12月的数据验证了该方法的有效性。研究发现：相关关键词的搜索热度指标具有领先CPI的预测作用,依此建立的CPI舆情指数有助于改进CPI预测精度。研究创新：揭示了基于相关关键词的搜索热度指标与CPI的非线性关系,提出了一种基于门限回归的CPI低频舆情指数构建方法;使用动态因子模型估计出了CPI高频舆情指数。研究价值：预测CPI时可辅助利用基于大数据构建的CPI低频与高频舆情指数信息。