The predictive distributions of thinning‐based count processes

This paper shows that the term structure of conditional (i.e. predictive) distributions allows for closed form expression in a large family of (possibly higher order or infinite order) thinning‐based count processes such as INAR(p), INARCH(p), NBAR(p), and INGARCH(1,1). Such predictive distributions are currently often deemed intractable by the literature and existing approximation methods are usually time consuming and induce approximation errors. In this paper, we propose a Taylor's expansion algorithm for these predictive distributions, which is both exact and fast. Through extensive simulation exercises, we demonstrate its advantages with respect to existing methods in terms of the computational gain and/or precision.     

“全面二孩”后的人口预期与政策展望

“全面二孩”生育政策究竟能对人口发展产生怎样的影响？文章假定今后维持“全面二孩”人口政策不变进行了人口预测。结果可知：城乡全面放开二孩政策并不会带来出生率的猛增和人口数量的剧烈反弹,高峰期最多可以新增出生人口400万;政策调整会提高人口峰值,有利于改善人口结构,延缓劳动力供给的衰减速度,但作用微乎其微。从其他国家的人口发展经验看,生育率的下降趋势似乎不可避免。因此,“全面二孩”并不是生育政策调整的终点,应继续监测生育水平,进一步调整生育政策;建立和完善“家庭友好”政策体系;从社会体制和经济体制应对负面影响。     

中国旅游业发展若干指标分析与预测

运用回归分析法分析了我国旅游业入境旅游人数、国内旅游人数和旅游总收入与时间及人均国民收入的关系,认为“三大旅游经济指标”与时间(年)和人均国民收入均存在极显著的正相关性。当人均国民收入增加1美元时,入境旅游人数将增加10.218万人次,国内旅游人数将增加71.595万人次,旅游总收入增加6.938亿元人民币;预测到2015年,入境旅游人数将接近2亿人次,国内旅游人数突破20亿人次大关,旅游总收入也将突破2万亿元人民币,年平均增长率分别为9.10%,6.51%和11.16%。     

专门人才预测的信息采集和处理

专门人才预测是制定教育规划的基础.为提高预测的准确性.应在定量预测技术中注重信息采集和处理两个环节.本文用实例论述了人才预测信息采集的注意点,信息指标的内涵外延,以及处理人才预测信息数据的方法.     

基于灰色线性组合模型的农产品物流需求预测

以北京市农产品物流需求为研究对象,采用灰色 GM(1,1)模型为基本方法,使用 Matlab 软件,借助蚁群算法求出了单个模型的权数,构建出灰色线性组合模型,对"十三五"时期北京市农产品的物流需求进行预测。研究发现：北京市农产品物流需求在"十三五"期间稳定增加。因此,要平衡"十三五"期间北京市农产品物流的供给与需求,应增加农产品物流的供给,即完善农产品物流基础设施建设、大力支持农产品物流企业的发展和政府加强宏观调控。预测的结果表明：灰色线性组合模型的拟合度较好,比其他传统方法有更高的预测精度。     

关于企业产品销售量的模糊预测

本文在分析了各种产品销售量预测模型的基础上,提出了采用模糊预测法的建议,剖析了模糊时间序列预测模型的基本算法,应用实例验证了模型的有效性。     

金融危机对房地产业影响趋势预测研究——以2008年金融危机为例

构建了基于系统动力学的金融危机对房地产业影响的趋势预测模型,以2008年金融危机为例模拟了金融危机持续不同时间的情况下,中国房地产业从业人员和房地产业GDP的变化趋势.研究结果表明,金融危机减少了中国房地产业从业人员数量和房地产业GDP,危机持续时间对房地产业从业人员的影响小于对房地产业GDP的影响.     

Aligning supply chain collaboration using Analytic Hierarchy Process

The significance of collaboration among supply chain members has been sufficiently stressed in the recent literature as a powerful tool for increasing accuracy of demand forecasts and for consequent cost reductions. Since it has been recognized that naïve forecasting is no longer cost efficient, Supply Chain (SC) members have found it very important to exchange relevant information that will help improve accuracy of demand forecasting. This information differs widely in terms of their characteristics. For example, some information (e.g. historic sales data) that is cheap to exchange may not contribute to a great increase in forecast accuracy. Similarly, some information may not be very reliable (e.g. demand forecast by individual SC members). In general, there is a trade-off in the kind of information required and the kind of information exchanged. This study analyses these trade-offs using an Analytic Hierarchy Process (AHP) model. The model is then implemented based on case studies conducted in two manufacturing firms. The AHP model ranks available information in terms of their contributions to improve forecast accuracy, and can provide vital clues to SC partners for preparing exchangeable data. From the case studies using AHP model, it was proved that using the preferred SC data, the firms could enhance forecasts accuracy. This in turn can help the firms to make decisions on SC collaborative arrangements for information exchange.     

折扣系数法在建立预测模型中的应用

本文针对不同观测点对预测值的不同影响,采用折扣系数法探讨了用折扣最小二乘法和折扣最小一乘法建立预测模型的方法以及用折扣系数法建立组合预测模型的方法。本文的方法不仅适合于一般趋势预测模型的建立,也可推广到一般回归预测模型的建立。这对于提高预测模型的预测精度有着重要的意义。     

地震灾害预防研究的思维方法与实践

国内外许多主流科学家认为地震预报是不可能的,但少数国内的科技工作者在此方面却取得了国际领先的成果。在预报地震这一世界性难题面前,我们不能盲目附和西方主流科学家的意见,应发扬中国传统文化中的整体论观点、生成论方法的优势,结合西方还原论的方法,不断进行新的探索。