灰色动态预测模型在货币供应量预测中的应用

在对金融系统中货币供应量的预测进行相关研究的基础上,介绍了符合金融系统预测规律的灰色系统动态预测法,并根据中国货币供应量的实际数据进行了实证的预测检验。结果显示,这种方法简单,不需要借助于其他任何时间序列数据,需要数据量少,预测精确度高,对于近期预测尤其准确,也可进行中长期预测;实证预测结果也符合我国金融系统中货币供应量预测的实际。     

二阶段供应链中提前期压缩的影响与协调

提前期是衡量业务过程绩效一个重要的因素,基于时间竞争的供应链管理战略核心就在于提前期的压缩,提前期压缩是形成供应链战略优势的有力来源.在市场需求预测精度随提前期变化的假设下,采用逻辑证明和数值分析的方法,研究由供应商-零售商构成的两阶段供应链中提前期压缩对供应链及其成员收益的影响以及如何实现供应链的渠道协调.研究结果表明:当供应链的服务水平小于0.5时,提前期的压缩可以获得供应链成员收益的Pareto改进;当供应链的服务水平大于等于0.5,引入线性转移支付补偿机制,可以实现供应链的渠道协调,获得供应链成员收益的Pareto改进.     

温斯特指数平滑法在水资源预测中的应用

水资源是人们赖以生存的必需物质。水资源的短缺已经严重影响经济的增长和人们的日常生活。文章基于温斯特指数平滑法理论,对我国九大流域水资源的使用做出预测模型,可以根据历史数据准确预测九大流域下一年度的用水总量和供水总量。这样,可以为管理部门制定符合实际的水资源调配政策提供依据,并且使九大流域地区的人们更高效地利用有限的水资源。     

The Value of Weather Information for E‐Commerce Operations

To be efficient, logistics operations in e‐commerce require warehousing and transportation resources to be aligned with sales. Customer orders must be fulfilled with short lead times to ensure high customer satisfaction, and the costly under‐utilization of workers must be avoided. To approach this ideal, forecasting order quantities with high accuracy is essential. Many drivers of online sales, including seasonality, special promotions and public holidays, are well known, and they have been frequently incorporated into forecasting approaches. However, the impact of weather on e‐commerce operations has not been rigorously analyzed. In this study, we integrate weather data into the sales forecasting of the largest European online fashion retailer. We find that sunshine, temperature, and rain have a significant impact on daily sales, particularly in the summer, on weekends, and on days with extreme weather. Using weather forecasts, we have significantly improved sales forecast accuracy. We find that including weather data in the sales forecast model can lead to fewer sales forecast errors, reducing them by, on average, 8.6% to 12.2% and up to 50.6% on summer weekends. In turn, the improvement in sales forecast accuracy has a measurable impact on logistics and warehousing operations. We quantify the value of incorporating weather forecasts in the planning process for the order fulfillment center workforce and show how their incorporation can be leveraged to reduce costs and increase performance. With a perfect information planning scenario, excess costs can be reduced by 11.6% compared with the cost reduction attainable with a baseline model that ignores weather information in workforce planning.     

股指期货信息内含股价变动信息的挖掘 ――小波框架与支持向量回归的金融建模应用

 内容提要：中国股指期货的推出指日可待，交易者多了一种投资工具的同时也带来了新的风险。建立准确的金融时间序列预测模型是逐利及避险的方法之一，一直是学者专家研究的热点。本研究结合小波转换与支持向量回归，提出一个二阶段时间序列预测模型。先以离散小波框架将预测变量分解成不同尺度的多个子序列，揭示隐藏在预测变量内的信息，再以支持向量回归为工具，以这些子序列为预测变量建构SVR模型。本研究以日经225指数开盘价为预测目标，以期货开盘价为预测变量对模型进行实证研究，结果显示，该模型的预测绩效比单纯SVR模型及随机漫步模型好。未来可尝试以不同的基底函数作进一步研究。     

Reverse Regression and Employment Discrimination

The importance of interval forecasts is reviewed. Several general approaches to calculating such forecasts are described and compared. They include the use of theoretical formulas based on a fitted probability model (with or without a correction for parameter uncertainty), various “approximate” formulas (which should be avoided), and empirically based, simulation, and resampling procedures. The latter are useful when theoretical formulas are not available or there are doubts about some model assumptions. The distinction between a forecasting method and a forecasting model is expounded. For large groups of series, a forecasting method may be chosen in a fairly ad hoc way. With appropriate checks, it may be possible to base interval forecasts on the model for which the method is optimal. It is certainly unsound to use a model for which the method is not optimal, but, strangely, this is sometimes done. Some general comments are made as to why prediction intervals tend to be too narrow in practice to encompass the required proportion of future observations. An example demonstrates the overriding importance of careful model specification. In particular, when data are “nearly nonstationary,” the difference between fitting a stationary and a nonstationary model is critical.     

A New Model of Trend Inflation

This article introduces a new model of trend inflation. In contrast to many earlier approaches, which allow for trend inflation to evolve according to a random walk, ours is a bounded model which ensures that trend inflation is constrained to lie in an interval. The bounds of this interval can either be fixed or estimated from the data. Our model also allows for a time-varying degree of persistence in the transitory component of inflation. In an empirical exercise with CPI inflation, we find the model to work well, yielding more sensible measures of trend inflation and forecasting better than popular alternatives such as the unobserved components stochastic volatility model. This article has supplementary materials online.     

Myopic Analysis for Multi‐Echelon Inventory Systems with Batch Ordering and Nonstationary/Time‐Correlated Demands

We provide an exact myopic analysis for an N‐stage serial inventory system with batch ordering, linear ordering costs, and nonstationary demands under a finite planning horizon. We characterize the optimality conditions of the myopic nested batching newsvendor (NBN) policy and the myopic independent batching newsvendor (IBN) policy, which is a single‐stage approximation. We show that echelon reorder levels under the NBN policy are upper bounds of the counterparts under both the optimal policy and the IBN policy. In particular, we find that the IBN policy has bounded deviations from the optimal policy. We further extend our results to systems with martingale model of forecast evolution (MMFE) and advance demand information. Moreover, we provide a recursive computing procedure and optimality conditions for both heuristics which dramatically reduces computational complexity. We also find that the NBN problem under the MMFE faced by one stage has one more dimension for the forecast demand than the one faced by its downstream stage and that the NBN policy is optimal for systems with advance demand information and stationary problem data. Numerical studies demonstrate that the IBN policy outperforms on average the NBN policy over all tested instances when their optimality conditions are violated.     

基于分形理论的时尚商品价格预测问题研究

在研究时尚商品价格规律及影响因素的复杂性、不确定性的基础之上，提出了基于分形理论的时尚商品价格预测新方法。该方法首先运用重标极差法分析了时尚商品价格时间序列预测的可行性，然后根据分形统计模型，得到时尚商品历史价格时间序列的分形维数，通过不断增加新销售或预测得到新的记录方法，求得增加新记录后的新分形维数，由此可以预测出下一时间单位的商品销售价格。通过实例进行了方法检验和结果比较，取得了较为理想的预测结果，证明了该方法的可行性和有效性。     

Forecasting mortality rates via density ratio modeling

The authors propose a semiparametric approach to modeling and forecasting age‐specific mortality in the United States. Their method is based on an extension of a class of semiparametric models to time series. It combines information from several time series and estimates the predictive distribution conditional on past data. The conditional expectation, which is the most commonly used predictor in practice, is the first moment of this distribution. The authors compare their method to that of Lee and Carter.