网络结构与企业外包网络绩效研究

应用计算机仿真,探讨了影响外包网络绩效的因素。首先抽象出企业间存在的几种网络结构(链式图、星形图、完全图和环形图),建立模型并利用计算机仿真,研究了网络中节点间的距离、网络信息传播能力和行业生产成本对于整个网络外包平均利润(或利润率)、平均承接价格(或承接价格比)及交易频率的影响。     

软环境对高新区创新网络运转的影响

高新区发展的关键在于在其内部培育出发达的创新网络。只有在高新区培育出较为成熟的创新网络,高新区才能获得内在的发展动力,不断地产生技术创新,实现可持续发展。但能否在区内尽快培育出较为成熟的创新网络,还主要与创新网络发育所依托的软环境有关。一、高新区创新网络的运转及完善过程高新区创新网络的运转及完善过程如下(见图)。图高新区创新网络的运转及完善过程在高新区的软、硬环境的基础上,通过网络运行机制的激发和调节,区内的高新技术企业、大学、科研机构、政府等行为主体之间建立了创新网络。在网络上,技术、人才、资金等资源…     

创业企业社会网络演化图谱研究——基于阶段与动机差异的分析

创业企业社会网络演化图谱子图具有连续递推性、内部包络性以及外部延伸性等特征.按照演化的动机来源可以将创业企业社会网络演化图谱划分为情感型、工具型与功利型演化图谱.基于动机定位差异性的创业企业社会网络情感型、工具型与功利型演化图谱具有结构层级、反复迭代、有限延伸、关系周期、能级跃迁等共性,并分析了其差异性,并由此揭示了基于动机定位差异性的创业企业社会网络情感型、工具型与功利型演化图谱形成的轨迹.     

消费合作社网络经营系统架构

一、消费合作社网络经营系统架构 消费合作社网络经营系统的模型图如图所示。该系统模型主要由三个子系统构成,分别是：信息系统、交易系统和物流系统。这三个子系统依次连接,就构成了消费合作社网络经营系统,同时也是一个完整的购物过程。     

简化复杂债务网络图的几种方法

通过定义几种变换,把复杂债务网络图变为有向二分图,给出清偿企业债务所需的最少资金。并通过变换进一步简化债务网络有向二分图,使债务线索更加清晰、简单。     

人工智能辅助设计：基于深度学习的 风景园林平面识别与渲染

采用风景园林学与人工智能的跨学科研究方式,开发了一种将深度学习模型——生成对抗网络(Generative Adversarial Network,GAN)用于风景园林平面图用地识别与图像渲染的新应用场景。以325张细致标注的平面方案图建立用于深度学习的数据集,训练循环生成对抗网络(CycleGAN)实现平面图不同用地类型地块的提取任务,以及平面色块图到色彩肌理图的渲染生成。进一步从图片质量、正确规范性和色彩表达等方面评价模型的识别与渲染结果。该训练模型有潜力被应用于风景园林案例的用地类型分析及平面渲染,帮助设计师提升分析及制图效率。     

知识经济条件下的主流企业模式——虚拟企业

一、虚拟企业的产生背景 Ｗｉｌｌｉａｎ Ｄａｖｉｄｏｗ和ＭｉｃｈａｅｌＳ．Ｍａｌｏｎｅ在１９９２年这样界定了虚拟企业的概念：“虚拟企业是由一些独立的厂商、顾客甚至同行的竞争对手通过信息技术连成的临时网络组织,以达到共享技术、分摊费用以及满足市场需求的目的。它既没有中央办公室,也没有正式的组织图,更不象传统企业那样具有多层次的组织结构。”可见,所谓虚拟企业（Ｖｉｒｔｕａｌ Ｂｕｓｉｎｅｓｓ）是若干成员企业为共同获得某个市场机遇而组成的动态联盟,其基本实质在于突破企业的界限,在全球范围内对企业内部和外…     

海洋创新区域合作网络演化分析

随着中国海洋经济的迅猛发展,对创新及合作提出了更高的要求。运用社会网络分析法,分四个阶段绘制分析中国海洋创新区域合作网络演化图。结果表明,中国海洋创新区域合作网络更趋密集化,网络中包含区域不断增加,而区域的创新合作伙伴数量、与伙伴的创新合作次数均得到了大幅度的提高。另外,区域的内部合作创新与外部合作创新也同向大幅度提高,而整体的态势为区域更倾向与其他区域合作创新。     

浅析计算机网络运行故障解决办法

随着计算机基础的迅猛发展，网络技术的应用已非常普及，笔者就经常遇到的计算机网络运行故障提出解决办法，供网络维护中参考。为叙述方便，给出示例的网络拓补图如下：     

网络环境下的会计教学方法刍议

随着信息时代的到来，信息的交流纽带主要依靠网络。计算机网络的快速发展，给企业理财环境带来了新的变化。因此，计算机网络技术广泛应用于会计教育教学当中是知识经济时代的要求。可见，利用网络激发和培养学生的学生兴趣、提高和掌握会计专业的基本理论、基本技能是摆在广大一线教师面前的重要使命。文章结合个人多年工作实践针对网络环境下的会计教学方法改革谈几点个人看法，与同行共勉。     

PSO-based high order time invariant fuzzy time series method: Application to stock exchange data

Fuzzy time series methods are effective techniques to forecast time series. Fuzzy time series methods are based on fuzzy set theory. In the early years, classical fuzzy set operations were used in the fuzzy time series methods. In recent years, artificial intelligence techniques have been used in different stages of fuzzy time series methods. In this paper, a novel fuzzy time series method which is based on particle swarm optimization is proposed. A high order fuzzy time series forecasting model is used in the proposed method. In the proposed method, determination of fuzzy relations is performed by estimating the optimal fuzzy relation matrix. The performance of the proposed method is compared to some methods in the literature by using three real world time series. It is shown that the proposed method has better performance than other methods in the literature.     

Seasonality in macroeconomic time series

Recently, the seasonal characteristics of macroeconomic time series have drawn a lot of attention. It has been argued that the seasonal component of many macroeconomic time series constitutes a major part of the series measured as a proportion of the variance. In addition it has been found that the seasonal component of most macroeconomic time series is constant and best “explained” by seasonal dummies. Specifically it is often found that a Christmas boom is followed by a beginning of the year trough. Based on quarterly and monthly macroeconomic time series from a large number of countries this paper shows that many macroeconomic time series have seasonal components that are changing over time. Furthermore, the Christmas boom and especially the 1st quarter trough is not found nearly as often as one might expect.     

Forecasting nonstationary time series—Some methodological aspects

The Box-Jenkins approach to time series analysis, which is an efficient way of analyzing stationary time series, recommends differencing as a general method for transforming a nonstationary time series into a stationary one. This paper gives a methodological discussion of some other ways of transforming a nonstationary series, in particular removing linear trends. It is argued that in many cases removing trends is superior to differencing in several respects. For example, when the process generating the time series is an ARMA(p,q) process added to a linear trend, differencing will produce an ARMA(p,q + 1) process that violates the invertibility conditions and is therefore difficult to estimate. The discussion is extended to time series with seasonal patterns.     

Measuring business cycles: A wavelet analysis of economic time series

Multiresolution wavelet analysis is a natural way to decompose an economic time series into trend, cycle, and noise. The method is illustrated with GDP data. The business-cycle component of the wavelet-filtered series closely resembles the series filtered by the approximate bandpass filter.     

季节时间序列理论发展综述

随着社会的进步,统计数据由过去的年度数据变为如今的季度、月度和日度数据,有些以实时交易为基础的超高频金融数据达到了按秒为间隔的频率,这些数据被称为季节时间序列。季节时间序列研究已经成为近十年来经济计量学和统计学中的热点,Joumal of Econometrics（1993,volume 55）就此问题进行了专题讨论。本文按照历史发展顺序对季节性时间序列理论进行了系统地介绍,并对这一领域的前沿热点问题进行了评述和展望。     

时间序列模型描述个体经济发展趋势的局限性及改进方法

趋势是经济时间序列最主要的特征之一。利用时间序列模型不仅能够分析经济序列的趋势，同时还可以进行预测。但利用时间序列模型对总体的经济趋势的估计不能用来描述个体的经济趋势。因此，研究描述个体经济趋势的改进方法是必要的。     

Testing for Seasonal Stability in Unemployment Series: International Evidence

Recently, it has been shown that seasonal and business cycles are related and a similar economic mechanism is at work in producing both types of cycles (Miron 1996). Thus, an analysis of seasonal fluctuations sheds light on the nature of the business cycle. This paper uses the classical test developed by Hylleberg et al. and the LM-type tests proposed by Canova and Hansen (1995) to investigate seasonal behavior in the unemployment series of Australia, Canada, Japan, New Zealand, the US and a number of OECD countries. The main findings are that the Australian, Austrian and Canadian series are non-stationary at all seasonal frequencies, French, Japan, the NZ and the UK series are stationary at all seasonal frequencies and the USA series is stationary only at the annual frequency. The test results for other series are mixed, suggesting that further analysis is required to reach a definite conclusion. The series, except for France, Japan, New Zealand and the UK, appear to possess unstable seasonal patterns, indicating changing business cycle conditions.     

THE CHOICE OF THE AGGREGATION LEVEL IN THE ESTIMATION OF QUARTERLY NATIONAL ACCOUNTS

Many central statistical offices use indirect time series disaggregation methods to produce quarterly national accounts estimates or other high frequency variables. This paper investigates the relation existing between the statistical properties of indirectly estimated time series and the contemporaneous aggregation level at which estimation is carried out, when a version of the Chow-Lin (1971, 1976) method is used to evaluate quarterly time series. It is shown that estimation at the lowest possible level of contemporaneous aggregation is not always optimal. In order to choose the level of contemporaneous aggregation at which time series disaggregation should be carried out, the use of formal econometric tests is suggested.     

A Study of Longterm Memory in Stock Market Prices Using Rescaled Range Analysis

Most of the studies on the behaviourof the Indian stock market using the autocorrelation function have revealed that the stock market is weakly efficient and the time series of stock prices and stock indices are random walks. The autocorrelation function assumes Gaussian or near-Gaussian properties in the underlying distribution. The distribution function is assumed to have the normal bellshaped curve. Mandelbrot [1972] has proved that the autocorrelation function works well in determining short-term dependence only. But it tends to underestimate long-run correlation for non-Gaussian series. Alternatively the Rescaled Range Analysis is used to study the long-term dépendance in the time series. The Rescaled Range Analysis (R/S Analysis) is a nonparametric methodology developed by H. E. Hurst, a British hydrologist in 1951. Originally this methodology was applied to study the long-term storage capacity of reservoirs and later it was extended to study many natural systems. This statistical methodology is used for distinguishing random time series from biased random time series (Fractal time series) and to study the persistence of trends and also the presence of periodic and nonperiodic cycles in a time series. In this paper a study of the Indian stock market is carried out using the method of Rescaled Range Analysis and Hurst Coefficient. We conclude that the series of stock prices have persistent behaviour. Nearly 18% of the stock prices are influenced by the past. This ‘memory effect’ in the case of stock indices is found to be 23%. The stock market has shown persistent trends and that the series of prices and indices are biased random walks. The present prices are influenced by the past prices and this influence goes across time scales, one period influencing all the subsequent periods.

     

The role of the log transformation in forecasting economic variables

For forecasting and economic analysis many variables are used in logarithms (logs). In time series analysis, this transformation is often considered to stabilize the variance of a series. We investigate under which conditions taking logs is beneficial for forecasting. Forecasts based on the original series are compared to forecasts based on logs. For a range of economic variables, substantial forecasting improvements from taking logs are found if the log transformation actually stabilizes the variance of the underlying series. Using logs can be damaging for the forecast precision if a stable variance is not achieved.