C++ Builder下时间型数据的处理

在各种应用软件中,时间型数据的处理特别重要,不同的软件开发环境对该类数据的处理方式并不相同。文中介绍了C++Builder下时间型数据的存储及常见操作,并给出了具体的操作实例,所介绍的方法已在自来水公司营业收费系统中得到了较好应用。

随机缺失函数型时间序列数据的k近邻回归估计

针对响应变量随机缺失的强混合函数型时间序列数据,首次提出了非参数回归模型的k近邻估计,并在一些正则条件下建立了k近邻回归算子的几乎完全一致收敛速度.此研究不仅推进了函数型非参数模型的理论研究,也为函数型数据的实际应用领域提供了理论支撑.

矩阵型截面数据时间序列自回归模型

矩阵型截面数据时间序列的优点在于可以同时刻画多个对象的多个属性.本文重点研究了矩阵型截面数据时间序列的自回归模型,给出了该模型的参数估计、模型识别、白噪声检验三个方面的理论结果.最后再利用矩阵型截面数据时间序列自回归模型,对两支银行股的日收益率序列和日成交量变化率序列进行建模分析.

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.

Selection model of trip time for rural population

Referring to the 1 248 survey data of rural population in 14 provinces of China, the influencing factors of trip time choice were analyzed. Based on the basic theory of disaggregate model and its modelling method, nine grades were selected as the alternatives of trip time, the variables affecting time choice and the method getting their values were determined, and a multinomial logit (MNL) model was developed. Another 1 200 trip data of rural population were selected to testify the model's validity. The result shows that the maximum absolute error of each period between calculated value and statistic is 3.6%, so MNL model has high calculation accuracy.

Evaluating the impact of spatio-temporal scale on CPUE standardization

This study focused on the quantitative evaluation of the impact of the spatio-temporal scale used in data collection and grouping on the standardization of CPUE(catch per unit effort).We used the Chinese squid-jigging fishery in the northwestern Pacific Ocean as an example to evaluate 24 scenarios at different spatio-temporal scales,with a combination of four levels of temporal scale(weekly,biweekly,monthly,and bimonthly)and six levels of spatial scale(longitude×latitude:0.5°×0.5°,0.5°×1°,0.5°×2°,1°×0.5°,1°×1°,and 1°×2°).We applied generalized additive models and generalized linear models to analyze the24 scenarios for CPUE standardization,and then the differences in the standardized CPUE among these scenarios were quantified.This study shows that combinations of different spatial and temporal scales could have different impacts on the standardization of CPUE.However,at a fine temporal scale(weekly)different spatial scales yielded similar results for standardized CPUE.The choice of spatio-temporal scale used in data collection and analysis may create added uncertainty in fisheries stock assessment and management.To identify a cost-effective spatio-temporal scale for data collection,we recommend a similar study be undertaken to facilitate the design of effective monitoring programs.

A Real-time Data Model Based on Temporal Data

Real-time database systems contain not only transaction timing constraints, but also data timing constraints. This paper discusses the temporal characteristics of data in real-time databases and offers a definition of absolute and relative temporal consistency. In real-time database systems, it is often the case that the policies of transaction schedules only consider the deadline of real-time transactions, making it insufficient to ensure temporal correctness of transactions. A policy is given by considering both the deadlines of transactions and the “data deadline” to schedule real-time transactions. A real-time relational data model and a real-time relational algebra based on the characteristics of temporal data are also proposed. In this model, the temporal data has not only corresponding values, but also validity intervals corresponding to the data values. At the same time, this model is able to keep historical data values. When validity interval of a relation is [NOW, NOW], real-time relational algebra will transform to traditional relational algebra.

Large Data Based Research on the Editing of Film Trailer

Movie trailers originate from the movies themselves. Although compared with the two-hour-long films, the trailers are insignificant. It takes certain skills to make moderate spoilers and sufficient gimmicks in this short period of time, while at the same time to hoist the audience appetite. Films are a result of cutting, so are trailers. But it is obviously insufficient for us to analyze their relationship only from the angle of editing techniques and art. This study is based on the perspective of large data, in which the clips of the trailers are abstracted in visual scenes by comparing the lenses of different tracks in editing software. In this way, we can get the time code of the lenses of the feature. Furthermore, when putting back the time code of the trailers into the movies again, we can have a vivid diagram of the trailers and film by using these time code and directly reveal the relationship between the trailers and the features.

Modeling the growth and policy implications of Global System for Mobile Telecommunication （GSM） in Nigeria

This paper investigated the growth and policy implications of Global System for Mobile Communication in Nigeria. Stochastic economic modeling was used to analyze Nigeria＇s time series data. The models were adjudged reliable before they were used. The components of the model were defined and a prior expectation of the relationship among the variables explained for the purpose of giving the reviewers and users a deep insight into the phenomenon under study. The secondary data used for the study were processed using the E-View for windows electronic packages. The outcome of the empirical and stochastic investigations shows that Global System for Mobile Communication has a positive relationship with output growth in Nigeria. The impact is of a higher magnitude. The usage of Global System for Mobile Telecommunication led to 17 percent rise in the output growth. The findings suggest the need for the Nigerian Communication Commission （NCC） and the federal government of Nigeria to expand tele-density and directly make telephone communications cheap and accessible. To achieve this goal, more licenses should be given to GSM operators in order to allow for healthy competition among them. This will lead to improved quality of services, quality of product and consequently sustain the growth and development of the country.

Fortified Financial Forecasting Models Based on Non-Linear Searching Approaches

The paper＇s aim is how to forecast data with variations involving at times series data to get the best forecasting model. When researchers are going to forecast data with variations involving at times series data （i.e., secular trends, cyclical variations, seasonal effects, and stochastic variations）, they believe the best forecasting model is the one which realistically considers the underlying causal factors in a situational relationship and therefore has the best ＂track records＂ in generating data. Paper＇s models can be adjusted for variations in related a time series which processes a great deal of randomness, to improve the accuracy of the financial forecasts. Because of Na＇fve forecasting models are based on an extrapolation of past values for future. These models may be adjusted for seasonal, secular, and cyclical trends in related data. When a data series processes a great deal of randomness, smoothing techniques, such as moving averages and exponential smoothing, may improve the accuracy of the financial forecasts. But neither Na＇fve models nor smoothing techniques are capable of identifying major future changes in the direction of a situational data series. Hereby, nonlinear techniques, like direct and sequential search approaches, overcome those shortcomings can be used. The methodology which we have used is based on inferential analysis. To build the models to identify the major future changes in the direction of a situational data series, a comparative model building is applied. Hereby, the paper suggests using some of the nonlinear techniques, like direct and sequential search approaches, to reduce the technical shortcomings. The final result of the paper is to manipulate, to prepare, and to integrate heuristic non-linear searching methods to serve calculating adjusted factors to produce the best forecast data.

Is China’s Economic Growth Profitled or Wage-led?——A Theoretical Inquiry and Empirical Study on the Impact of Labor Share on Growth

The paper makes a comparative review and evaluation of different theories on impacts of the distribution of labor and capital income on economic growth as well as criticism of the mainstream theories and suggests using Bhaduri-Marglin model as the theoretical basis for relevant research. By formulating the econometric model and analyzing time series data from 1978 to 2012, the paper makes an empirical research into the impact of labor share on economic growth in China. It has discovered that the positive effect of labor share on consumption demand growth in China outweighs its negative effect on investment and net export growth, concluding that growth in China is wage-led at present. That may mean that （1） increasing labor share promotes growth and the goals of equality and efficiency supports each other,＂ （2） the demand and optimizing structure＂ lies in regulation tools. key to ＂stabilizing growth, expanding domestic establishing a set of pro-labor institution and

An Empirical Study on the stock Price of Modeling

the model in time series analysis are widely used in the field of economy. We often use the model in time series to analyze data, but without regard to the rationality of the model. In this paper, we introduce and analyze Ping An Of China（601318） shares at the opening price（2013/01/04-2013/07/04）.The model is established by analyzing data. Modeling steps of ARIMA model and GARCH model are presented in this paper. The data whether ARIMA model is suitable by white noise. Or the data whether GARCH model is suitable by since the correlation of variance test. By comparing the analysis, it selects a more reasonable model.

对志书数据记述的一点思索

对志书数据记述的一点思索浙江省海门市委组织部保健中新方志应是一部朴实、严谨、科学的资料著述。但纵观各地志书，在数据的记述上“硬伤”颇多，使志书的权威性和资料的可靠性受到影响。有一些评价尚好的志书，据说还有请统计专业人士把关，也未能囿出于此。其实，志书...

Survival analysis of car travel time near a bus stop in developing countries

This paper presents an important investigation into car travel time affected by mixed traffic flow near a bus stop on the basis of survival analysis theory.Travel time data associated with mixed traffic characteristics near a bus stop were collected by video cameras.A hazard-based duration model was introduced to analyze the effects of mixed traffic flow on car travel time.The results indicate that mixed traffic flow impacts car travel time significantly.And the presence of bus berthing violation would delay car travel time.The proposed model can be used to forecast temporal shifts in car travel time due to changes in mixed traffic flow.The influential factors related to mixed traffic flow should be given full consideration in the planning and designing of bus stops in developing countries.

AN INTERVAL METHOD FOR STUDYING THE RELATIONSHIP BETWEEN THE AUSTRALIAN DOLLAR EXCHANGE RATE AND THE GOLD PRICE

This paper proposes an interval method to explore the relationship between the exchange rate of Australian dollar against US dollar and the gold price, using weekly, monthly and quarterly data. With the interval method, interval sample data are formed to present the volatility of variables. The ILS approach is extended to multi-model estimation and the computational schemes are provided. The empirical evidence suggests that the ILS estimates well characterize how the exchange rate relates to the gold price, both in the long-run and short-run. The comparison between the interval and point methods indicates that the difference between the OLS and the ILS estimates is increasing from weekly data to quarterly data, since the lowest frequency point data lost the most information of volatility.