Prediction accuracy for reservation-based forecasting methods applied in Revenue Management

With a few notable exceptions, airlines and hospitality forecasting research has been focused so far on point predictions of customers’ bookings. However, Revenue Management decisions are subject to a much greater risk when based exclusively on point predictions. To overcome this drawback, we propose a stochastic framework that allows the construction of prediction intervals for reservation-based (pickup) forecasting methods, which are widely used in the industry. Moreover, we introduce an extension of the multiplicative pickup technique based on Generalized Linear Models. We test the proposed framework with real reservation data from a medium-sized hotel on Lake Maggiore (Italy) and we obtain more efficient prediction intervals relative to classical time series methods. Our approach can be useful to hotel revenue managers that wish to make more informed decisions, planning alternative pricing and room allocation strategies for a range of possible demand scenarios.     

Long horizon predictability of exchange rates: Is it for real?

This paper reexamines recent results on the predictability of nominal exchange rate returns by means of fundamental models. Using a monthly sample of the post-Bretton Woods period we show that the in-sample fit between long-horizon exchange rate returns and various models is not significant if we correct for the persistence that is caused by overlapping data and spurious regression phenomena. The long horizon out-of-sample predictive power of the fundamental exchange rate models is found to be very weak. This is especially the case when we conduct the out-of-sample forecasting tests for a longer time span than that of earlier papers. We show that this failure in forecasting performance, resulting from extending the time span, is due to the absence of cointegration between exchange rates and structural exchange rate models. First version received: September 1997/final version received: November 1998     

Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination

In this paper, we examine the forecast accuracy of linear autoregressive, smooth transition autoregressive (STAR), and neural network (NN) time series models for 47 monthly macroeconomic variables of the G7 economies. Unlike previous studies that typically consider multiple but fixed model specifications, we use a single but dynamic specification for each model class. The point forecast results indicate that the STAR model generally outperforms linear autoregressive models. It also improves upon several fixed STAR models, demonstrating that careful specification of nonlinear time series models is of crucial importance. The results for neural network models are mixed in the sense that at long forecast horizons, an NN model obtained using Bayesian regularization produces more accurate forecasts than a corresponding model specified using the specific-to-general approach. Reasons for this outcome are discussed.     

基于灰色预测模型的安徽省经济发展与环境污染关系研究

采用灰色预测理论,对安徽省1991～2007年经济发展与环境污染的关系进行了研究。建立了关于两者的灰色预测模型,经检验模型具有较高的精度。对安徽省未来五年经济发展与环境污染预测结果表明,未来五年,安徽省除了第三产业比重下降外,其余经济发展与环境污染指标都有上升的趋势,这种"高增长、高污染"的经济增长方式是不符合可持续发展战略的。     

基于组合预测模型的长三角地区冷链产品产量预测

及时地预测冷链产品产量,对于冷链产品的生产与物流的良性发展具有重要的作用。文章以国家统计局数据为基础,分析了2002—2011年长三角地区冷链产品的产量趋势和产量结构,并利用最优加权法确定权重,建立了基于灰色预测模型,对组合预测模型进行误差分析,最后应用组合模型预测出2012—2016年长三角地区冷链产品的产量,在此基础上提出相关建议。     

上海旅游业发展的系统分析

面对本世纪初上海旅游发展中需要解决的一些实际问题，运用“系统分析”中的预测、评价及规划模型等手段，对国内外来护游客人数进行了预测；对影响接等能力的多种因素进行了层次分析；对如何开发旅游景点提出了“多指标优序”评价模型；对某个旅游点或某个饭店是否开发建设提出了“零一多目标”规划模型。     

Forecasting with trees

The prevalence of approaches based on gradient boosted trees among the top contestants in the M5 competition is potentially the most eye-catching result. Tree-based methods out-shone other solutions, in particular deep learning-based solutions. The winners in both tracks of the M5 competition heavily relied on them. This prevalence is even more remarkable given the dominance of other methods in the literature and the M4 competition. This article tries to explain why tree-based methods were so widely used in the M5 competition. We see possibilities for future improvements of tree-based models and then distill some learnings for other approaches, including but not limited to neural networks.     

浅论21世纪初期河南矿业的可持续发展策略

本文简述了河南省矿产资源勘查、开发现状及存在问题,从资源潜力角度分析了本省矿产资源对国民经济的可支撑能力;对20世纪下半叶矿业市场进行了简要回顾,对21世纪初期矿业进行了展望,并对21世纪初期主要矿产品走势进行了分析预测.在此基础上,提出了21世纪初期河南矿业的可持续发展若干策略建议.     

美国不确定性冲击对全球股市波动的影响研究

选取2000—2021年美国经济、金融、经济政策和地缘风险四类不确定性指数以及全球GDP排行前15国家股市收益率数据,基于多维不确定性冲击框架,运用单因子、双因子和多因子混频波动率GARCH-MIDAS模型,从样本内拟合与样本外预测两个方面实证考察美国不确定性冲击对全球主要国家股市波动的差异化影响。研究表明：美国经济不确定性和金融不确定性对多数国家股市长期波动均有正向推动作用,其中,美国金融不确定性的影响最为广泛;美国金融不确定性是影响中国股市长期波动的主要因素,且中美贸易摩擦主要通过美国金融不确定性传导;美国经济政策不确定性上升会增加俄罗斯和墨西哥股市长期波动,美国地缘风险对意大利股市长期波动存在显著正向影响。     

Hawaii tourism to the year 2000 : A Delphi forecast

The Delphi torecasting technique is used to forecast tourism to Hawaii, particularly Oahu, by the year 2000. Local experts and travel agents were questioned on visitor arrivals and percentage of domestic arrivals to Hawaii, market share, visitor-to-resident ratio, maximum visitor accommodation and desirable growth rates, and probable scenarios for Oahu tourism. The results show few significant differences in responses among the groups, and confirmed expectations about convergence and consistency of managerial responses with statistical projections and existing trends. As such, this study demonstrates the value of combining qualitative with quantitative techniques in making long-term forecasts.