时间序列分析的AR模型在全社会消费品零售总额预测中的应用

全社会消费品零售总额是一个与宏观经济运行状况有重要关系的经济变量,本文对全社会消费品零售总额作时间序列分析,建立AR模型进行趋势预测.我们以1987年至1995年的统计数据,作为时间序列分析的样本观测值,数据单位为人民币亿元,样本长度n=18.首先,假定全社会消费品零售总额x_t是一个平稳时间序列,为选择适当的描述x_t增长与变化规律的数字模型,我们对样本观测值进行数据分析。

经济预测讲座(二)如何利用时间序列分析进行经济预测

一、时间序列分析是经济预测的常用方法 利用时间序列分析的方法,对经济活动进行预测,在我国的预测方法中是应用得比较早,也是比较普遍的方法之一。在统计部门,由于实行定期连续地对社会经济现象进行调查统计的制度,统计基础资料比较系统、完整,所以利用时间序列分析方法进行经济预测,比起其它部门,具有很大优势。什么叫时间序列呢?所谓时间序列就是将表明某种现象的统计指标的统计数字,按照发生时间的先后顺序排列起来所形成的数列。它能反映某种现象发展变化的动态,所以也叫作动态数列。一组时间序列中的各项数字所代表的时间长短、调查统计的总体范围、计算方法和计量单位、指标所反映的质的内容,前后应该一致,即人们常说的统计口径是可比的,这样的时间序列才能用于经济分析和预测。 利用时间序列对经济进行预测。

1型CRISPR位点间区序列分析在嗜热链球菌菌株分型中的应用

为了建立适用于嗜热链球菌菌株资源多样性调查的菌株分型方法,尝试将1型CRISPR位点间区序列分析用于嗜热链球菌的菌株分型,并与常用ERIC-PCR指纹图谱方法进行了比较。结果表明,1型CRISPR位点间区序列分析可以把30株从三个不同样品中分离的嗜热链球菌分成三种差异明显的类型:不同类型菌株之间没有相同的间区序列;而同一类型菌株之间,间区序列的组成和排列则基本一致,并且上述分型的结果与用ERIC-PCR指纹图谱技术获得的结果完全一致。因此,1型CRISPR位点间区序列分析是嗜热链球菌分型鉴定的可靠方法,并适用于大量菌株的分型鉴定和多样性调查。

REP-PCR及ERIC-PCR法对分离自海产品副溶血性弧菌分型分析

目的:采用细菌基因外重复回文序列扩增(REP-PCR)和肠道细菌基因间重复序列扩增(ERIC-PCR)两种方法对副溶血性弧菌2株标准株,13株实验室保存菌株和73株分离菌株共88株进行分子分型。方法:通过REP和ERIC-PCR指纹图谱扩增,利用NTsys-pc软件采用Dice系数对指纹图谱进行聚类结果分析。结果:所有供试菌株可通过此两种方法进行分型得到清晰的指纹图谱,并反映出副溶血性弧菌具有丰富的遗传多样性,REP-PCR扩增出5～9条分布在609～4200bp之间的条带,可将副溶血性弧菌分为5个群12类型,分辨指数达0.93,其中tdh+株在相似系数0.86时可聚类在第Ⅰ群;ERIC-PCR扩增出5～11条分布在400～7593bp之间的条带,将副溶血性弧菌分为7个群11个类型,分辨指数为0.94,其中tdh+株在相似系数0.76时可聚类在第Ⅰ群。结论:两种方法均显现出很好的分型能力,能够很好地将环境分离tdh+菌株和标准菌株聚类在一起,其中REP-PCR较ERIC-PCR重复性更好。

自校正信息融合Wiener预报器及其收敛性

对带相关观测噪声和未知噪声统计的多传感器系统,用相关方法得到噪声统计在线估值器.在按分量标量加权线性最小方差最优信息融合准则下,用现代时间序列分析方法,基于滑动平均(moving average)新息模型的辨识,提出了自校正解耦融合Wiener预报器.用动态误差系统分析(dynamic error system analysis)方法证明了自校正融合Wiener预报器收敛于最优融合Wiener预报器,因而它具有渐近最优性.它的精度比每个局部自校正Wiener预报器精度都高.它的算法简单,便于实时应用.一个目标跟踪系统的仿真例子说明了其有效性.

Feature extraction and damage alarming using time series analysis

Aiming at the problem of on-line damage diagnosis in structural health monitoring （SHM）, an algorithm of feature extraction and damage alarming based on auto-regressive moving-average （ARMA） time series analysis is presented. The monitoring data were first modeled as ARMA models, while a principalcomponent matrix derived from the AR coefficients of these models was utilized to establish the Mahalanobisdistance criterion functions. Then, a new damage-sensitive feature index DDSF is proposed. A hypothesis test involving the t-test method is further applied to obtain a decision of damage alarming as the mean value of DDSF had significantly changed after damage. The numerical results of a three-span-girder model shows that the defined index is sensitive to subtle structural damage, and the proposed algorithm can be applied to the on-line damage alarming in SHM.

ITS1 SEQUENCES OF NUCLEAR RIBOSOMAL DNA IN WILD RICES AND CULTIVATED RICES OF CHINA AND THEIR PHYLOGENETIC IMPLICATIONS

The first internal transcribed spacer(ITS1) of nuclear ribosomal DNA of three wild rice species and two subspecies of cultivated rice, which are distributed in China, was amplified using PCR technique and sequenced with automated fluorescent sequencing. The sequences of ITS1 ranged from 193 bp to 218 bp in size and G/C content varied from 69.3% to 72.7%. In pairwise comparisons among the five taxa, sequence site divergence ranged from 1.5% to 10.6%. Phylogenetic analysis of ITS1 sequences using Wagner parsimony generated a single well resolved tree, which revealed that Oryza rufipogon was much more closely related to cultivated rice species than to the other two wild species. Oryza granulata was less closely related to either cultivated rice species or the other two wild species, and might be a unique and isolated taxon in the genus Oryza. The phylogenetic relationships of the three wild rice species and two cultivated rice subspecies inferred from ITS1 sequences is highly concordant with those based on the molecular evidence from isozyme, chloroplast DNA (cpDNA), mitochondrial DNA(mtDNA) and nuclear DNA (nDNA) of the genus Oryza .

RESEARCH ON MUNICIPAL WATER DEMANDS FORECAST

Based on the changing law of municipal water demand,a trigonometric function model for short-term water demand forecast is established using the time-series analysis approach.The method for forecasting water demand during holidays and under unexpected events is also presented.Meanwhile,a computer software is developed.Through actual application,this method performs well and has high accuracy,so it can be applied to the daily operation of a water distribution system and lay a foundation for on-line optimal operation.

生物强化MBR系统处理溴氨酸启动期特性研究

采用鞘氨醇单胞菌强化膜生物反应器(MBR),对模拟溴氨酸废水进行处理.考查了生物强化MBR启动期溴氨酸的降解、污泥特性及细菌生理状态的变化,并用DNA指纹技术——核糖体基因间区序列分析(RISA)揭示MBR启动期污泥的菌群变化.结果显示,采用生物强化可以使MBR迅速启动并稳定运行.在启动期,污泥浓度下降,沉降性和絮凝性变好;脱氢酶活性下降并稳定在一个较低的水平上;胞外聚合物中蛋白和多糖含量略有上升;启动期末期膜出水溴氨酸脱色率可达90%以上,COD去除率可达60%以上.RISA指纹分析表明污泥系统多样性减少,形成了降解溴氨酸的功能群落.

Prediction and Analysis of O_3 based on the ARIMA Model

The research conducted prediction on changes of atmosphere pollution during July 9, 2014-July 22, 2014 with SPSS based on monitored data of O3 in 13 successive weeks from 6 sites in Baoding City and demonstrated prediction effect of ARIMA model is good by Ljung-Box Q-test and R2, and the model can be used for prediction on future atmosphere pollutant changes.

Effects of Freezing Disaster on Green-up Date of Vegetation Using MODIS/EVI Time Series Data

In the field of global changes, the relationship between plant phenology and climate, which reflects the response of terrestrial ecosystem to global climate change, has become a key subject that is highly concerned. Using the moderate-resolution imaging spectroradiometer (MODIS)/enhanced vegetation index(EVI) collected every eight days during January- July from 2005 to 2008 and the corresponding remote sensing data as experimental materials, we constructed cloud-free images via the Harmonic analysis of time series (HANTS). The cloud-free images were then treated by dynamic threshold method for obtaining the vegetation phenology in green up period and its distribution pattern. And the distribution pattern between freezing disaster year and normal year were comparatively analyzed for revealing the effect of freezing disaster on vegetation phenology in experimental plot. The result showed that the treated EVI data performed well in monitoring the effect of freezing disaster on vegetation phenology, accurately reflecting the regions suffered from freezing disaster. This result suggests that processing of remote sensing data using HANTS method could well monitor the ecological characteristics of vegetation.

A Cross-Reference Method for Nonlinear Time Series Analysis in Semi-Blind Case

In this paper, we propose a cross reference method for nonlinear time series analyzing in semi blind case, that is, the dynamic equations modeling the time series are known but the corresponding parameters are not. The tasks of noise reduction and parameter estimation which were fulfilled separately before are combined iteratively. With the positive interaction between the two processing modules, the method is somewhat superior. Some prior work can be viewed as special cases of this general framework. The simulations for noise reduction and parameter estimation of contaminated chaotic time series show improved performance of our method compared with previous work.

辛勤耕耘半个世纪的李茂年教授

辛勤耕耘半个世纪的李茂年教授统计星座颜日初李茂年教授，现年８０岁，１９４２年毕业于中央政治学校大学部经济系统计专业（组）．１９４３年２月至今在高等学校担任教学工作，历任讲师、副教授、教授，１９８６年被国务院批准为博士生导师。现执教于中南财经大学。五十...

Gross errors identification and correction of in-vehicle MEMS gyroscope based on time series analysis

This paper presents a novel approach to identify and correct the gross errors in the microelectromechanical system （MEMS） gyroscope used in ground vehicles by means of time series analysis. According to the characteristics of autocorrelation function （ACF） and partial autocorrelation function （PACF）, an autoregressive integrated moving average （ARIMA） model is roughly constructed. The rough model is optimized by combining with Akaike＇s information criterion （A/C）, and the parameters are estimated based on the least squares algorithm. After validation testing, the model is utilized to forecast the next output on the basis of the previous measurement. When the difference between the measurement and its prediction exceeds the defined threshold, the measurement is identified as a gross error and remedied by its prediction. A case study on the yaw rate is performed to illustrate the developed algorithm. Experimental results demonstrate that the proposed approach can effectively distinguish gross errors and make some reasonable remedies.

Short-term forecasting optimization algorithms for wind speed along Qinghai-Tibet railway based on different intelligent modeling theories

To protect trains against strong cross-wind along Qinghai-Tibet railway, a strong wind speed monitoring and warning system was developed. And to obtain high-precision wind speed short-term forecasting values for the system to make more accurate scheduling decision, two optimization algorithms were proposed. Using them to make calculative examples for actual wind speed time series from the 18th meteorological station, the results show that: the optimization algorithm based on wavelet analysis method and improved time series analysis method can attain high-precision multi-step forecasting values, the mean relative errors of one-step, three-step, five-step and ten-step forecasting are only 0.30%, 0.75%, 1.15% and 1.65%, respectively. The optimization algorithm based on wavelet analysis method and Kalman time series analysis method can obtain high-precision one-step forecasting values, the mean relative error of one-step forecasting is reduced by 61.67% to 0.115%. The two optimization algorithms both maintain the modeling simple character, and can attain prediction explicit equations after modeling calculation.

Effects of Snow Cover on Ground Thermal Regime： A Case Study in Heilongjiang Province of China

The important effects of snow cover to ground thermal decades. In the most of previous research, the effects were usually regime has received much attention of scholars during the past few evaluated through the numerical models and many important results are found. However, less examples and insufficient data based on field measurements are available to show natural cases. In the present work, a typical case study in Mohe and Beijicun meteorological stations, which both are located in the most northern tip of China, is given to show the effects of snow cover on the ground thermal regime. The spatial （the ground profile） and time series analysis in the extremely snowy winter of 2012-2013 in Heilongjiang Province are also performed by contrast with those in the winter of 2011-2012 based on the measured data collected by 63 meteorological stations, Our results illustrate the positive （warmer） effect of snow cover on the ground temperature （GT） on the daily basis, the highest difference between GT and daily mean air temperature （DGAT） is as high as 32.35℃. Moreover, by the lag time analysis method it is found that the response time of GT from 0 cm to 20 cm ground depth to the alternate change of snow depth has 10 days lag, while at 40 cm depth the response of DGAT is not significant. This result is different from the previous research by modeling, in which the resnonse denth of ground to the alteration of snow depth is far more than 40 cm.

Oil–water two-phase flow pattern analysis with ERT based measurement and multivariate maximum Lyapunov exponent

Oil–water two-phase flow patterns in a horizontal pipe are analyzed with a 16-electrode electrical resistance tomography(ERT) system. The measurement data of the ERT are treated as a multivariate time-series, thus the information extracted from each electrode represents the local phase distribution and fraction change at that location. The multivariate maximum Lyapunov exponent(MMLE) is extracted from the 16-dimension time-series to demonstrate the change of flow pattern versus the superficial velocity ratio of oil to water. The correlation dimension of the multivariate time-series is further introduced to jointly characterize and finally separate the flow patterns with MMLE. The change of flow patterns with superficial oil velocity at different water superficial velocities is studied with MMLE and correlation dimension, respectively, and the flow pattern transition can also be characterized with these two features. The proposed MMLE and correlation dimension map could effectively separate the flow patterns, thus is an effective tool for flow pattern identification and transition analysis.

Time-series analysis with a hybrid Box-Jenkins ARIMA

Time-series analysis is important to a wide range of disciplines transcending both the physical and social sciences for proactive policy decisions. Statistical models have sound theoretical basis and have been successfully used in a number of problem domains in time series forecasting. Due to power and flexibility, Box-Jenkins ARIMA model has gained enormous popularity in many areas and research practice for the last three decades. More recently, the neural networks have been shown to be a promising alternative tool for modeling and forecasting owing to their ability to capture the nonlinearity in the data. However, despite the popularity and the superiority of ARIMA and ANN models, the empirical forecasting performance has been rather mixed so that no single method is best in every situation. In this study, a hybrid ARIMA and neural networks model to time series forecasting is proposed. The basic idea behind the model combination is to use each model’s unique features to capture different patterns in the data. With three real data sets, empirical results evidently show that the hybrid model outperforms ARIMA and ANN model noticeably in terms of forecasting accuracy used in isolation.

Chaotic characteristics of electromagnetic emission signals during deformation and fracture of coal

Electromagnetic emission(EME) is a kind of physical phenomenon accompanying the process of deformation and fracture of loaded coal and rock and it is of importance in quantitatively analyzing its characteristics.This will reveal the process of deformation and fracture of coal and predicting dynamic disasters in coal mines.In this study,the G-P(Grassberger and Procaccia) algorithm,calculation steps of the(if only 1 dimension) correlation dimension of time series and the identification standards of chaotic signals are introduced.Furthermore,the correlation dimensions of EME and the acoustic emission(AE) signals of time series during deformation and fracture of coal bodies are calculated and analyzed.The results show that the time series of pulses number of EME and the time series of AE count rate are chaotic and that the saturation embedding dimensions of a K3 coal sample are,respectively,5 and 6.The results can be used to provide basic parameters for predicting of EME and AE time series.

Runoff Change of Naoli River in Northeast China in 1955–2009 and Its Influencing Factors

Runoff change and trend of the Naoli River Basin were studied through the time series analysis using the data from the hydrological and meteorological stations. Time series of hydrological data were from 1957 to 2009 for Bao′an station, from 1955 to 2009 for Baoqing station, from 1956 to 2009 for Caizuizi station and from 1978 to 2009 for Hongqiling station. The influences of climate change and human activities on runoff change were investigated, and the causes of hydrological regime change were revealed. The seasonal runoff distribution of the Naoli River was extremely uneven, and the annual change was great. Overall, the annual runoff showed a significant decreasing trend. The annual runoff of Bao′an, Baoqing, and Caizuizi stations in 2009 decreased by 64.1%, 76.3%, and 84.3%, respectively, compared with their beginning data recorded. The wet and dry years of the Naoli River have changed in the study period. The frequency of wet year occurrence decreased and lasted longer, whereas that of dry year occurrence increased. The frequency of dry year occurrence increased from 25.0%-27.8% to 83.9%-87.5%. The years before the 1970s were mostly wet, whereas those after the 1970s were mostly dry. Precipitation reduction and land use changes contributed to the decrease in annual runoff. Rising temperature and water project construction have also contributed important effects on the runoff change of the Naoli River.