GM(1,1)模型在航材消耗预测中的应用

传统的预测方法很难对部分消耗数据少、影响因素不明确的航材进行准确预测。运用灰色GM(1,1)模型,可以对"小样本"、贫信息"这类航材进行更为准确的消耗预测。     

聚类分析在航材分类上的应用

针对航材种类繁多、规格复杂、消耗规律多样导致航材消耗研究较困难的问题,提出采用聚类分析方法根据历年航材消耗数据对大量航材进行分类,为避免量纲的影响采用马氏距离进行样本间相似性度量,为全面反映样本类间的相似性采用离差平方和进行类与类间的相似性度量。算例给出了利用聚类分析方法对一定量的样本进行分类的步骤和方法,结果表明分类效果显著。     

基于R语言SARIMA模型的航材需求预测分析

采用时间序列SARIMA模型对航材需求进行预测,以2010～2014年某航材实际月需求量数据为基础,运用R语言对航材需求量时间序列进行了稳定性判别;通过定阶和参数估计,构建了航材需求预测模型,并进行了数据预测。结果显示使用SARIMA模型拟合效果较好,预测能力可靠,能为航材部门需求预测提供准确方便的方法。     

基于马尔科夫分析的航材消耗预测

准确预测航材消耗量对保障飞行意义重大,航材消耗的特点符合马尔科夫过程。马尔科夫预测是关于事件发生概率预测的方法,通过示例分析表明在样本数据符合条件的情况下运用马尔科夫分析对航材消耗进行预测能够取得较好的预测效果。     

一体化联合作战航材物流保障模式研究

一体化联合作战的出现必将对航材保障模式带来重大影响,有必要研究探索新的航材保障模式。建立的航材物流保障模式是将第三方物流引入部队航材保障工作中,对提高空军航材保障能力具有重要的理论价值和现实意义。     

新机航材保障问题与对策

随着新型飞机逐步装备部队,新机航材保障的地位和作用愈加突出。做好新机航材保障,将为航空兵作战效能的提升起到积极作用。文章主要分析了当前新机航材保障面临的主要问题,并对问题提出了针对性的对策。     

基于ARIMA模型的航材需求预测

为了对航材的需求进行预测,本文根据时间序列乘积季节模型,利用统计软件SPSS,对收集到的航材需求的历史数据进行了建模、参数估计、检验、预测,经检验预测效果较好。该方法简便实用,利于实际推广和使用。     

航材库存管理现状及对策措施探讨

随着二代机等老旧机型逐步退役,新机相继列装部队,为了缓解航材库存管理日益凸显的问题,深入剖析航材库存管理现状,提出合理可行的对策措施,对于基层场站航材股优化库存结构、提升航材保障效能具有一定的推动作用.     

航材资源管理的研究进展:国内外文献综述

对航材资源的文献进行总结和梳理,对国内外相关研究进行了分析,并从航材预测、航材库存管理、航材供应链管理、航材仓库选址等方面进行了研究结果的总结与分析,提出了值得进一步研究的方向。     

全面推进航材仓库现代化建设的思考与对策

航材仓库是海军装备物资的保障基地,部队所需航材能否高效准确、及时安全地得到供应,与航材仓库管理工作密切相关。因此,加强航材仓库管理,提高仓库管理水平,对于提高航材保障能力,做好新时期军事斗争准备,加速我军现代化建设都具有重大的意义。本文分别从航材仓库的管理信息化、作业机械化、包装标准化、运输集装化和保障野战化等方面阐述了加强航材仓库现代化建设的主要措施和对策。     

基于支持向量机的鸡蛋供应链中价格预警研究

鸡蛋价格的大幅波动对鸡蛋供应链中生产者、经营者和消费者产生严重的影v向,因此鸡蛋供应链中的价格预警问题亟待研究。支持向量机是一种建立在统计学习理论基础上的机器学习方法,能够较好地解决小样本、非线性和局部极小点等问题。文中基于支持向量机方法建立鸡蛋价格预警模型,运用libsvm软件对样本集进行参数寻优、训练和测试。结果表明模型预警结果与实际情况相符,模型可以帮助鸡蛋供应链中的各主体在采购、生产、仓储、销售等方面更好地决策。     

基于支持向量机的轮胎中PU综合性能的预测应用

为了更好聚氨酯(PU)绿色轮胎原料的批量投产和产业化过程中进行实时的产品质量预测与控制打下了良好的基础。本文利用支持向量机对PU弹性体制备过程中的综合性能指标建立预测模型,同时获得较好的预测精度。     

CPPD案例教学法应用效果的一项实验研究

CPPD是案例教学的一种新方法。基于一项教学实验,本文运用问卷调查和统计分析,考察其在管理案例教学中的实际效果。结果显示,CPPD案例教学法在激发学习兴趣、活跃课堂气氛、获取专业知识、提升沟通技能、增强知识运用能力和批判性思维能力方面较传统案例教学法更具优势,在增强学习信心与记忆案例内容方面则与传统案例教学法没有显著差异。CPPD案例教学法较传统案例教学法更有利于发挥案例教学的潜在优势。     

A novel two-stage method for well-diversified portfolio construction based on stock return prediction using machine learning

The performance of portfolio model can be improved by introducing stock prediction based on machine learning methods. However, the prediction error is inevitable, which may bring losses to investors. To limit the losses, a common strategy is diversification, which involves buying low-correlation stocks and spreading the funds across different assets. In this paper, a diversified portfolio selection method based on stock prediction is proposed, which includes two stages. To be specific, the purpose of the first stage is to select diversified stocks with high predicted returns, where the returns are predicted by machine learning methods, i.e. random forest (RF), support vector regression (SVR), long short-term memory networks (LSTM), extreme learning machine (ELM) and back propagation neural network (BPNN), and the diversification level is measured by Pearson correlation coefficient. In the second stage, the predictive results are incorporated into a modified mean–variance (MMV) model to determine the proportion of each asset. Using China Securities 100 Index component stocks as study sample, the empirical results demonstrate that the RF+MMV model achieves better results than similar counterparts and market index in terms of return and return–risk metrics.     

The role of the museum shop in extending the visitor experience

• The aim of the research is to assess the role of the museum shop in extending the learning experience in the museum. The research is situated in the theoretical framework of museum experiences and learning. It acknowledges environmental influences on learning, and of consumption sites that synthesise leisure and consumption and create memorable sensory experiences that engage customers.
• The exploratory approach uses mixed methods to examine visitors' knowledge and experience of museums and their shops. The first phase of the research was undertaken with in-depth interviews to scope the experience of museum shops and their contribution to learning. The second phase was undertaken with visitors to the Imperial War Museum (IMW) in London through a convenience sample which elicited 150 responses to a semi-structured questionnaire. The research demonstrates the significance of the museum shop as a destination for museum visitors, and its role in creating experiences that can supplement the museum's educational priorities and a return to the everyday world of familiar objects.

Copyright © 2009 John Wiley & Sons, Ltd.     

二元经济结构视角下民生性财政支出与消费关系研究

习近平总书记指出目前中国正处于新发展阶段,我们要加快构建新发展格局,扩大内需,刺激消费。在此背景下,论文依托中国2013-2019年31个省相关的统计数据,利用个体固定效应模型分析民生性财政支出各组成部分对城乡居民消费水平的共性和差异化影响。研究表明,环境保护和住房保障支出对于城乡居民消费水平都没有显著的作用,而教育支出、社会保障和就业这两项支出对城镇居民消费水平的作用较为显著,医疗卫生支出对农村居民来说是正向的。     

基于自适应学习的地块定价策略研究

基于最小二乘法的自适应地块价值评估策略,本文将机器学习模型与专家经验提炼的逻辑框架相结合,通过机器学习去全量化、自动化学习框架参数可以有效提高定价精度,改进后的定价系统可以达到商用级别精度(90％以上),较专家定价,具有成本低、效率高、精度强的优点,较纯机器学习模型定价,高质量样本量的需求大幅减少,达到低成本、高效率、...     

One Possible Frame for Thinking about Experiential Learning

I argue that teaching statistical thinking is harder than teaching mathematics, that experimental design is particularly well suited to teaching statistical thinking and that in teaching statistics, variation is good. We need a mix of archival data, simulations and activities, of varying degrees of complexity. Within this context, I applaud the important contributions to our profession represented by Darius et al. (2007) , and Nolan & Temple Lang (2007) , the first for showing us how to make simulation‐based learning simultaneously more flexible and more realistic than ever before, and the second for showing us a path‐breaking technology that can make archival data the basis for active learning at an impressively high level of sophistication, embedding statistical thinking within real scientific and practical investigations.     

Machine learning in M4: What makes a good unstructured model?

The Makridakis Competitions seek to identify the most accurate forecasting methods for different types of predictions. The M4 competition was the first in which a model of the type commonly described as “machine learning” has outperformed the more traditional statistical approaches, winning the competition. However, many approaches that were self-labeled as “machine learning” failed to produce accurate results, which generated discussion about the respective benefits and drawbacks of “statistical” and “machine learning” approaches. Both terms have remained ill-defined in the context of forecasting. This paper introduces the terms “structured” and “unstructured” models to better define what is intended by the use of the terms “statistical” and “machine learning” in the context of forecasting based on the model’s data generating process. The mechanisms that underlie specific challenges to unstructured modeling are examined in the context of forecasting, along with common solutions. Finally, the innovations in the winning model that allowed it to overcome these challenges and produce highly accurate results are highlighted.     

Bootstrapping rank statistics

The bootstrap, which provides powerful approximations for many classes of statistics, is studied for simple linear rank statistics employing bounded and smooth score functions. To verify consistency we view a rank statistic as a statistic induced by a statistical functional ψ which is evaluated at a pair of dependent signed measures. Thus, we can apply the von Mises method to verify asymptotic results for the bootstrap. The strong consistency of the bootstrap distribution estimator is derived for the bootstrap based on resampling from the original data. Further, the residual bootstrap is studied. The accuracy of the bootstrap approximations for small sample sizes is studied by simulations. The simulations indicate that the bootstrap provides better results than a normal approximation.