Automatic selection of a linear predictor through frequency domain cross-validation

Given data from a weakly stationary stochastic process in discrete time, and any L-step ahead linear predictor estimated from that data, we will construct an approximately unbiased estimator of the resulting mean squared error of L-step ahead linear prediction. The motivation for the estimator is based on frequency domain cross-validation, and hence the range of validity and applicability of the resulting selection method is not limited by particular assumptions about the structure of the underlying stochastic process or the form of the fitted linear predictors. We also propose a new frequency domain predictor fitting method. The method provides a natural finite-past analog to the existing spectral factorization techniques, and it compares favorably with the existing techniques, both asymptotically and for finite samples. In a Monte Carlo study, we compare several predictor selection methods, at lead times one and five. The performance criterion used is the mean squared prediction error of the selected predictor. The new selection methods work well, and a comparison of results for the two different lead times underscores the need for tailoring the selection criterion to suit the lead time.     

市场预测支持系统的预测模型研究

本文运用模糊数学和统计相关的理论,预测支持系统确定预测量的相关因子,分析了建造预测模型的过程,给出了待预测量的预测模型,并验证之。     

浙江人口发展前景及战略选择

本文根据1990年"四普"资料和1995年1％人口抽样调查资料,在分析人口发展现状的基础上对浙江未来人口发展趋势进行了预测,并对浙江跨世纪的人口发展战略进行了探索,提出在保持适度低生育率、继续控制人口总量的前提下,宜谨慎地调整生育政策,以避免人口过于老化;根据浙江人口现状和'科教兴省'的战略思想,宜适时地将人口发展战略重点转移到提高人口素质上来。     

集团军山地进攻作战减员预计模型

收集了我军以往作战军减员的经验数据,建立军减员率的时间序列模型,提出计算机模拟的方法。在此基础上,着重分析影响减员的多种因素,对交战双方武器装备数量及技术等级,作战地区的地形和气候条件等因素进行了定量描述。运用专家咨询方法筛选了社会经济行为等"软"指标,用群体层次分析法确定各指标的权重,建立了量化指标体系,并运用该指标体系对我军今后主要作战对象进行了量化。结合以上因素对计算机模拟生成的数据进行修正,建立相应的调整算法。     

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.