基于贝叶斯结构时间序列模型的海南省人口预测研究

通过分析2003—2022年的人口数据,深入了解海南省的人口现状,并预测常住人口总量。数据源于《海南省统计年鉴》,采用折线图、人口金字塔图等对海南省2003—2022年人口现状进行描述,并应用贝叶斯结构时间序列模型对未来三年的常住人口数进行预测。经过分析和预测,结果显示,贝叶斯结构时间序列模型的均方根误差为2.914、平均绝对百分比误差为0.002<10、决定系数为0.986。2023—2025年海南省常住人口总数分别为1042.34万人、1054.56万人、1066.76万人。说明贝叶斯结构时间序列模型应用于海南省常住人口预测的效果较好,海南省常住人口数量仍会持续缓慢上升。

基于时间序列分析的工业控制以太网流量异常检测

为提高工业网络中异常流量的检测精度,提出了基于结构时间序列分析的流量异常检测方案,将工业以太网流量分解成不同组分,并辅以状态空间模型,将复杂的网络流量进行分层建模,从而有效提高了工业网络异常流量检测精度,降低了误报率.与传统的X-12结构时间序列分析法相比,其平均精度上升38%,所以本文方法对于异常检测系统的效率改善明显.

以生存时间为疗效指标的临床试验转组研究中RPSFT法与BW法进行处理效应估计的性能比较

目的:本文介绍等级结构保留失效时间模型(rank preserving structural failure time models,RPSFT)和BW法(the method of Branson and Whitehead)在临床试验转组研究中的应用,并对两种方法进行比较。方法:以部分对照组受试者在试验中途转至试验组为例,采用计算机模拟试验,查看不同截尾率、转组率水平下,RPSFT法和BW法估计试验药疗效的效果。以及探讨与传统的ITT法相比,RPSFT法和BW法的检验效能和Ⅰ类错误控制情况。结果:RPSFT法和BW法估计试验药疗效的准确性高,与传统的分析方法比较,引入的偏倚小。随着转组率和截尾率增大,RPSFT法与BW法的偏倚逐渐增大,估计的试验药疗效低于真实疗效,但RPSFT法的偏倚比BW法小。两种方法估计的均方误差(MSE)十分接近。当截尾率较大(40%)时,RPSFT法的MSE比BW小。随着转组率增大,两种方法的Ⅰ类错误不断升高,普遍高于0.05。与ITT法相比,RPSFT法与BW法的检验效能受转组率的影响小,下降趋势缓慢。结论:在估计试验药疗效的临床试验中,若存在对照组受试者转组到试验组的情况,统计分析以ITT法分析为主,RPSFT法和BW法作为辅助分析方法。RPSFT法和BW法相比,当截尾率和转组率均较高时,参数估计优先考虑选择使用RPSFT法。

结构时间序列模型在经济预测方面的应用研究

本文开发了一种新的经济时间序列预测方法———利用结构时间序列模型进行预测。在结构时间序列模型中由经济指标分解得到的趋势、循环、季节及不规则因素是不可观测的变量,不能利用传统的回归分析方法求解模型,因此,本文采用状态空间方法来求解结构时间序列模型。本文通过ARIMA模型来研究经济时间序列的结构,在此基础上建立了不同形式的结构时间序列模型,并利用结构时间序列模型对我国社会消费品零售总额、狭义货币供给量(M1)和国内生产总值(GDP)等经济时间序列进行了预测。实证研究表明,结构时间序列模型具有良好的预测效果,从而为经济时间序列预测提供了一种新的有效方法。

结构时间序列模型在季节调整方面的应用——与X-12季节调整方法的比较分析

建立一种基于结构时间序列模型的新的时间序列季节调整方法.首先,利用ARIMA模型研究时间序列的结构,根据序列的单整阶数(d)建立趋势循环分量的表达式,并在此基础上构建不同形式的结构时间序列模型.在结构时间序列模型中,针对经济指标分解得到的趋势、循环、季节及不规则因素是不可观测的变量,不能利用回归分析方法求解模型,因此,采用状态空间形式来求解模型.最后,利用结构时间序列模型对我国国内生产总值(GDP)和社会消费品零售总额等宏观经济时间序列进行了季节调整,并与目前广泛使用的X-12季节调整方法进行对比分析,实证结果表明,基于结构时间序列模型的季节调整方法具有相对较强的稳定性.

基于结构时间序列模型的季节调整方法研究

一、季节调整的重要意义 月度或季度经济时间序列一般可分解为四种变动要素,即长期趋势要素T,循环要素C,季节变动要素S和不规则要素I。季节变动要素和循环要素的区别在于,季节变动要素是每年重复出现的周期变动,是由温度、降雨、年内的月份、假期、政策等引起的,而循环要素是间距比较长且不固定的一种周期性波动,它代表景气波动。经济时间序列分解模型也称为结构时间序列模型。依据时间序列的四个构成要素在模型中的相互关系,可以表现出多种不同的形式,一般而言,基本的分解模型为加法模型和乘法模型。设经济时间序列为{y1}。

基于贝叶斯结构时间序列模型评估我国梅毒流行趋势

目的探讨贝叶斯结构时间序列(BSTS)模型在预测我国梅毒流行趋势中的应用价值。方法收集2005年1月至2022年8月我国梅毒发病数据,采用Eviews10软件解析梅毒月发病数据中的趋势和季节组分;采用RStudio软件构建模型,其中2005年1月至2021年12月的数据作为训练集拟合BSTS模型,2022年1—8月数据作为测试集验证模型的预测效果,并将其预测准确性与求和自回归滑动平均混合(ARIMA)模型进行比较。所有统计学分析检验水准指定为α=0.05。结果我国梅毒发病总体呈上升态势,具有周期性和季节性,每年1—2月为低谷,7—8月为高峰。在训练和测试集上,BSTS模型产生的平均绝对百分比误差(分别为4.95%和5.73%)均小于ARIMA模型(分别为5.44%和6.52%);同样也发现,BSTS模型产生的平均绝对误差、均方根误差和均方根百分比误差均小于ARIMA模型。稳健性结果表明了同样的结果。基于BSTS模型预测的2022年9月至2023年12月我国梅毒发病总数为719600[95%置信区间(CI):605295~826086]例,月均发病数为44975(95%CI:37831~51631)例。结论我国梅毒是一种季节性疾病,发病仍处于高水平;BSTS模型能准确评估我国梅毒动态流行趋势,可为梅毒精准防控提供技术支撑。

我国西部世界遗产地门票价格变动对客源市场的影响——以甘肃省和四川省世界遗产地为例

旅游需求的显著特点之一是随时间变化而变化,定量研究和预测旅游需求随时间的变化,对世界遗产地的科学开发规划和经营决策具有重要的指导意义.选取甘肃和四川两省世界遗产地景点的门票价格,统计分析了世界遗产地门票价格变动对客源市场的调节作用,用时间结构分析模型探讨门票价格变动引起的客源市场季节性结构特征和年际结构特征.结果表明:门票价格的上涨对世界遗产地的客源市场在短期内具有一定影响,但长期影响不显著.要解决目前世界遗产地人满为患的困境,尚需积极寻求其它有效措施.

季节调整方法综述及比较

Seasonal adjustment has been widely used in statistic analyses.Nowadays,the research on seasonal adjustment methods mainly concentrates on X-11,X-12 etc in China,lacking the whole understanding of foreign seasonal adjustment methods,and the latest progress of seasonal adjustment methods has been less introduced.In this article,various seasonal adjustment methods were introduced,and a comparison of their characteristics and applications was made.It is helpful that statistical organizations can develop appropriate seasonal adjustment methods,concerning different kinds of data.

季节调整方法在中国的发展与应用

随着我国社会主义市场化经济程度的不断提高,人们逐渐开始使用环比数据监控宏观经济重要指标,而环比数据质量的好坏取决于对季节调整方法的了解与掌握程度。在简要介绍了季节调整方法及相应软件的发展现状之后,本文对季节调整研究在我国的发展与应用状况进行了总结。在季节调整发展部分,文章主要介绍了中国人民银行PBC版X-12-ARIMA季节调整软件和国家统计局版NBS-SA季节调整软件的特点;在季节调整应用部分,则分别以春节效应和结构时间序列模型为主线对我国季节调整文献进行了梳理。最后在小结部分,本文给出了一些建设性意见。

我国电力周期的单变量状态空间分析

随着我国经济的快速增长,国民经济对电力的需求随之快速增长。为了在快速增长的同时保持电力增长的稳定性,了解电力周期的波动情况,成了重要的问题。本文利用单变量状态空间法对改革开放以来的电力需求数据进行分析,建立结构时间模型,利用单变量状态法找出变化趋势,分析我国电力需求周期情况,为预测未来电力需求走势,提前做好应对措施提供了依据。

The Influence of Particle Interactions on the Existence of Quantum Particles Properties

We propose a novel model, based on two postulates, which provide new perspective on the fundamental forces using special and general relativity concepts. Many studies address the relations between the particles and the space time manifold, and the latter＇s physical structure, whether it is Continuous or Discrete. In the proposed model the properties of the particles are classical in the sense of general relativity, whereas their quantum properties are arises due to the experiments.

A new quantitative structure-retention relationship model for predicting chromatographic retention time of oligonucleotides

An integrated approach is proposed to predict the chromatographic retention time of oligonucleotides based on quantitative structure-retention relationships （QSRR） models. First, the primary base sequences of oligonucleotides are translated into vectors based on scores of generalized base properties （SGBP）, involving physicochemical, quantum chemical, topological, spatial structural properties, etc.; thereafter, the sequence data are transformed into a uniform matrix by auto cross covariance （ACC）. ACC accounts for the interactions between bases at a certain distance apart in an oligonucleotide sequence; hence, this method adequately takes the neighboring effect into account. Then, a genetic algorithm is used to select the variables related to chromatographic retention behavior of oligonuclcotides. Finally, a support vector machine is used to develop QSRR models to predict chromatographic retention behavior. The whole dataset is divided into pairs of training sets and test sets with different proportions; as a result, it has been found that the QSRR models using more than 26 training samples have an appropriate external power, and can accurately represent the relationship between the features of sequences and structures, and the retention times. The results indicate that the SGBP-ACC approach is a useful structural representation method in QSRR of oligonucleotides due to its many advantages such as plentiful structural information, easy manipulation and high characterization competence. Moreover, the method can further be applied to predict chromatographic retention behavior of oligonucleotides.

Feature Analysis and Modeling of the Network Community Structure

Community structure has an important influence on the structural and dynamic characteristics of the complex systems.So it has attracted a large number of researchers.However,due to its complexity,the mechanism of action of the community structure is still not clear to this day.In this paper,some features of the community structure have been discussed.And a constraint model of the community has been deduced.This model is effective to identify the communities.And especially,it is effective to identify the overlapping nodes between the communities.Then a community detection algorithm,which has linear time complexity,is proposed based on this constraint model,a proposed node similarity model and the Modularity Q.Through some experiments on a series of real-world and synthetic networks,the high performances of the algorithm and the constraint model have been illustrated.

Ultrafast quasiparticle dynamics in spin-density-wave LaOFeAs single crystal

Ultrafast quasiparticle dynamics of single crystalline LaOFeAs were investigated by pump-probe measurement.The compound experiences structural and spin-density-wave(SDW)phase transitions at 150 K(TS1)and 130 K(TS2),respectively.The relaxation time of quasiparticles was somewhat temperature independent at high temperature but exhibited a sharp upturn at TS1and reached the maximum at approximately TS2.The remarkable slowing down of quasiparticle relaxation time is caused by the formation of energy gap.By employing the Rothwarf-Taylor model analysis,we found that there should be already energy gaps opening just below the structural transition.The magnitude of SDW gap was identified to be 72 meV.

Modelling envelope and temporal fine structure components of frequency-following responses in rat inferior colliculus

In studies of auditory perception, a dichotomy between envelope and temporal fine structure(TFS) has been emphasized. It has been shown that frequency-following responses(FFRs) in the rat inferior colliculus can be divided into the envelope component(FFREnv)and the temporal fine structure component(FFRTFS). However, the existing FFR models cannot successfully separate FFREnv and FFRTFS. This study was to develop a new FFR model to effectively distinguish FFREnv from FFRTFS by both combining the advantages of the two existing FFR models and simultaneously adding cellular properties of inferior colliculus neurons. To evaluate the validity of the present model, correlations between simulated FFRs and experimental data from the rat inferior colliculus were calculated. Different model parameters were tested, FFRs were calculated, and the parameters with highest prediction were chosen to establish an ideal FFR model. The results indicate that the new FFR model can provide reliable predictions for experimentally obtained FFREnv and FFRTFS.

Size influence on the fluorescence decay time of ZnS:Mn^(2+) nanocrystals

ZnS:Mn2+ nanocrystals(NCs) with particle size from 1.9 nm to 3.2 nm are synthesized via chemical precipitation method with different [S2-]/[Zn2+] ratios.The size-dependent decay for Mn emission exhibits a double exponential behavior.And two lifetime values,in millisecond time domain,can both be shortened with size increasing,which is attributed to enhanced interaction between host and Mn2+ impurity.A molecular structure model is proposed to interpret the tendency of two lifetime components,which is correlated to the number of S vacancy(Vs) defects around Mn2+.