Holt—Winters模型与X—11模型在预测中的比较研究

以实际数据为例，对一时间序列分别建立Holt-Winter模型与X－11模型并进行预测，从而对这两种统计进行比较研究，得出对于既有趋势又有季节性的时间序列进行预测X－11模型优于Holt-Winers模型。     

X-11方法在出生人口数量预测中的应用

用X-11方法,以1973至1981年加拿大出生人口的数据为例,通过分析出趋势成分T和季节成分S及不规则成分项E.对1982年出生人口进行预测,并探讨了这种方法在人口预测中的适用性.     

X-11方法在出生人口数量预测中的应用

用X-11方法，以1973至1981年加拿大出生人口的数据库例，通过分析出趋势成分T和季节成分S及不规则成分项E。对1982年出生人口进行预测，并探讨了这种方法在人口预测中的适用性。     

ARIMA模型在地区GDP预测中的应用研究

在时间序列分析理论的基础上,以山东省2007年第一季度到2011年第四季度的GDP总值为基础,利用SPSS软件,对数据进行时间序列分析,建立时间序列分析模型,并对模型进行检验。利用建立的模型对山东省在2012年的GDP的增长趋势做出预测。     

时间序列模型在CPI预测中的应用

利用时间序列模型对全国2000年5月到2012年4月的月度居民消费价格指数（consumption priceindex,CPI）建立了自回归移动平均模型（ARMA）。并对2012年4月的CPI进行了预测,结果表明,ARMA（1,1）是描述我国CPI变化趋势相对较优的时间序列模型。     

ARIMA模型在广西消费价格指数分析与预测中的应用

根据运用软件建立ARIMA（1,1,1）求和自回归移动模型,并分析广西消费价格指数（CPI）变化规律,为广西消费价格指数（CPI）提供较好的预测,对政府宏观政策的实施提供重要依据。     

基于ARIMA模型的青岛市GDP预测分析

国内生产总值(GDP)是衡量一个国家经济状况的重要指标,对促进经济增长和协助地方经济决策具有重要意义。本文以青岛市为例,选取青岛市1990年至2014年青岛市GDP指标数据作为样本,建立了ARIMA模型,并对青岛市GDP进行预测,并进行检验。结果表明模型的预测数与实际GDP值误差较小。同时根据对影响GDP的指标分析为青岛市经济决策提供重要的依据。     

时间序列长期趋势线性预测比较分析

文章针对常用的三种线性模型即二次移动平均法、二次指数平滑法、一元线性回归方程,因其采用不同的方法修均不规则波动,其预测效果有所差别.对比分析说明三种线性模型的应用条件.     

基于ARIMA模型的苎麻纤维产量预测研究

为了解长期定位试验下苎麻纤维产量的变化趋势,推动苎麻种植产业增产提效,基于苎麻多倍体1号品种2010—2019年共30个收获期的纤维产量数据,构建ARIMA纤维产量预测模型,并对模型精度进行验证。结果表明：ARIMA(2, 0, 3)模型最佳,4个收获期中纤维产量预测值与实测值的平均相对误差百分比为7.72%,整体预测效果较好,适用于该地区多倍体1号纤维产量的短期预测。     

神经网络在经济预测中的应用

对神经网络理论在时间序列建模和预测中的应用进行了探索．对基于ＢＰ网络及ＫＲＢＦ网络的预测模型进行了分析，并通过对股票价格序列模拟试验得出神经网络对非线性时间序列的预测较传统方法具有一定的优越性．同时神经网络预测模型具有自适应学习和并行处理功能，并可应用于多维时间序列．对经济预测具有重要意义     

不同分布下CPI模型比较与预测

应用不同分布下的ARCH类模型对1990-2013年的居民消费价格指数建立模型,对其不同分布下的ARCH类模型进行对比分析.分别在正态分布,T分布,广义误差分布下建立了ARCH模型,发现在不同的分布下其ARCH模型有不同的预测效果,得到了在广义误差分布下建立的GARCH模型最为精确.     

A Note on the Use of Mixture Models for Individual Prediction

Mixture models capture heterogeneity in data by decomposing the population into latent subgroups, each of which is governed by its own subgroup-specific set of parameters. Despite the flexibility and widespread use of these models, most applications have focused solely on making inferences for whole or subpopulations, rather than individual cases. This article presents a general framework for computing marginal and conditional predicted values for individuals using mixture model results. These predicted values can be used to characterize covariate effects, examine the fit of the model for specific individuals, or forecast future observations from previous ones. Two empirical examples are provided to demonstrate the usefulness of individual predicted values in applications of mixture models. The first example examines the relative timing of initiation of substance use using a multiple event process survival mixture model, whereas the second example evaluates changes in depressive symptoms over adolescence using a growth mixture model.     

Generalized Nonlinear Irreducible Auto-Correlation and Its Applications in Nonlinear Prediction Models Identification

There is still an obstacle to prevent neural network from wider and more effective applications, i.e. , the lack of effective theories of models identification. Based on information theory and its generalization, this paper introduces a universal method to achieve nonlinear models identification. Two key quantities, which are called nonlinear irreducible auto-correlation (NIAC) and generalized nonlinear irreducible auto-correlation (GNIAC), are defined and discussed. NIAC and GNIAC correspond with intrinstic irreducible auto-dependency (IAD) and generalized irreducible autodependency (GLAD) of time series respectively. By investigating the evolving trend of NIAL; and GNIAC, the optimal auto-regressive order of nonlinear auto-regressive models could be determined naturally, Subsequently, an efficient algorithm computing NIAC and GNIAC is discussed. Experiments on simulating data sets and typical nonlinear prediction models indicate remarkable correlation between optimal auto-regressive order and the highest order that NIAC-GNIAC have a remarkable non-zero value, therefore demonstrate the validity of the proposal in this paper.     

Evaluating the Predictive Value of Growth Prediction Models

This study evaluates four growth prediction models—projection, student growth percentile, trajectory, and transition table—commonly used to forecast (and give schools credit for) middle school students' future proficiency. Analyses focused on vertically scaled summative mathematics assessments, and two performance standards conditions (high rigor and low rigor) were examined. Results suggest that, when “status plus growth” is the accountability metric a state uses to reward or sanction schools, growth prediction models offer value above and beyond status‐only accountability systems in most, but not all, circumstances. Predictive growth models offer little value beyond status‐only systems if the future target proficiency cut score is rigorous. Conversely, certain models (e.g., projection) provide substantial additional value when the future target cut score is relatively low. In general, growth prediction models' predictive value is limited by a lack of power to detect students who are truly on‐track. Limitations and policy implications are discussed, including the utility of growth projection models in assessment and accountability systems organized around ambitious college‐readiness goals.     

低周疲劳寿命预测模型分析

综合分析了几种低周疲劳寿命预测模型,特别是对能量法则的寿命预测模型进行了讨论。评述了各种不同预测方法的基本假设、特点和应用范围。叙述了各种低周疲劳寿命预测模型的计算步骤,并提出实际应用中应考虑的问题。该文对低周疲劳寿命预测,尤其是对航空高温合金具有较大的工程参考价值。文中所论述的三参数能量预测模型具有易懂、简捷的工程应用前景。     

时间序列法在高校新生入学率预测中的应用

采用平滑线散点图分析某高校招生部门提供的2005～2009年分省、直辖市所录取新生入学报到率历史数据,应用基于时间序列法的新生入学报到率预测模型预测了2010年14个省、直辖市新生入学报到率,与实际报到率相比较结果显示由模型预测出的2010年新生报到率绝大部分处于置信区间．该模型可为高校招生部门在各省、直辖市投放招生指标提供一种定量决策方法．     

国际教师专业标准的三种模式及启示

教师专业化是国际教师教育发展的必然趋势.从某种意义上说,教师专业化既是一种目的,是教师教育所要追求的目标;也可以说是一种过程,即培养教师使其达到专业所要求标准的过程,而教师专业标准的模糊或缺失必然导致教师专业化发展方向的迷失.本文主要围绕国际上三种模式的教师专业标准,分析其内容及主要特征,在此基础上,提出我国建立教师专业标准的政策建议,以全面提高我国教师培养培训的质量.     

基于多种数学模型下的湖南省人口预测研究

区域发展的主体是人,同时区域发展的目的是以人为本。在PILED战略中,人口特别是人力资源的开发是一个关键因素,是关系区域经济、社会、城市化、区域规划等多个因素的重大问题。文章采用自回归模型、马尔萨斯人口模型以及Logistic模型等多种数学分析方法,对湖南省2009-2020年的人口数量做出了科学预测,为湖南省在新的历史机遇期制定相关社会经济发展战略与规划提供相应的依据。     

Latent Curve Models and Latent Change Score Models Estimated in R

When using multiple imputation in the analysis of incomplete data, a prominent guideline suggests that more than 10 imputed data values are seldom needed. This article calls into question the optimism of this guideline and illustrates that important quantities (e.g., p values, confidence interval half-widths, and estimated fractions of missing information) suffer from substantial imprecision with a small number of imputations. Substantively, a researcher can draw categorically different conclusions about null hypothesis rejection, estimation precision, and missing information in distinct multiple imputation runs for the same data and analysis with few imputations. This article explores the factors associated with this imprecision, demonstrates that precision improves by increasing the number of imputations, and provides practical guidelines for choosing a reasonable number of imputations to reduce imprecision for each of these quantities.