A Review of Some Modern Approaches to the Problem of Trend Extraction

This article presents a review of some modern approaches to trend extraction for one-dimensional time series, which is one of the major tasks of time series analysis. The trend of a time series is usually defined as a smooth additive component which contains information about the time series global change, and we discuss this and other definitions of the trend. We do not aim to review all the novel approaches, but rather to observe the problem from different viewpoints and from different areas of expertise. The article contributes to understanding the concept of a trend and the problem of its extraction. We present an overview of advantages and disadvantages of the approaches under consideration, which are: the model-based approach (MBA), nonparametric linear filtering, singular spectrum analysis (SSA), and wavelets. The MBA assumes the specification of a stochastic time series model, which is usually either an autoregressive integrated moving average (ARIMA) model or a state space model. The nonparametric filtering methods do not require specification of model and are popular because of their simplicity in application. We discuss the Henderson, LOESS, and Hodrick–Prescott filters and their versions derived by exploiting the Reproducing Kernel Hilbert Space methodology. In addition to these prominent approaches, we consider SSA and wavelet methods. SSA is widespread in the geosciences; its algorithm is similar to that of principal components analysis, but SSA is applied to time series. Wavelet methods are the de facto standard for denoising in signal procession, and recent works revealed their potential in trend analysis.     

DSSS系统中自适应时变小波包干扰抑制

提出了一种基于自适应时变小波包变换实现直接序列扩频通信系统时变窄带干扰抑制的方法。该方法在自适应时域分解的基础上,利用小波包变换多分辨分析能力和良好的时频特性进行频率分解,将有用信号和干扰分离,然后将受扰频带置零后进行小波包合成,达到滤除时变窄带干扰的目的。计算机仿真结果表明,该方法与非时变自适应小波包抑制技术相比较,以计算复杂度为代价,能更灵活有效地跟踪和定位时变干扰,提供较好的误码率性能。     

快速路由器的路由查找和流分类算法研究

分析了路由器的体系结构发展,研究了路由查找算法和流分类算法在快速路由器中的应用。研究表明,基于分段压缩的路由查找算法支持IPv6路由查找,具有合理的存储容量和快速的查找时间;采用按值分支树的多维综合流分类算法支持前缀和范围匹配,可扩展性强,适合大容量规则数据库。两种算法适合在快速路由器中应用。     

视听传播的艺术观照

"超文本"的电视文化高速发展表现在两个方面:一是通过国际卫星接收国际频道,提升全球化、国际化水平;二是有线电视、家庭录像机和影碟机的发展,扩大人们的选择余地。自我创造并参与中国文化创造的中国电视文化正在全面走向成熟,其影响力表现在三个方面:一是由集体性转向个体性话语,确认"个人主体"价值;二是将传统文化移入全球性文化空间,促进中国"现代性"焦虑形成;三是经历了由无到有、由少至多的高速成长过程,具有极强的过渡性。其定位也由模糊到明确。由于"技术滤器"作用电视具有特殊的表现形式和工作方式,传播文化革命冲击了电视新闻、记录片、体育、文艺、广告等节目形态。     

基于最优估计理论的上市公司永久性盈余估计

估计上市公司的永久性盈余对进行股票投资是非常重要的.本文应用最优估计理论中的卡尔曼滤波和强跟踪滤波方法对上市公司的永久性盈余进行动态估计.实证结果表明基于强跟踪滤波的估计比基于卡尔曼滤波的估计精确许多.     

CREDIBILITY THEORY: A CONCISE PARTIAL SURVEY1

Developments in credibility theory in the last seventy years or so are outlined. Particular attention is paid to early work in the field and to more recent developments showing some of the many connections between credibility theory and other fields including linear Bayesian theory, linear filtering theory and constant coefficient regression.     

Dynamic financial distress prediction based on Kalman filtering

In models for predicting financial distress, ranging from traditional statistical models to artificial intelligence models, scholars have primarily paid attention to improving predictive accuracy as well as the progressivism and intellectualization of the prognostic methods. However, the extant models use static or short-term data rather than time-series data to draw inferences on future financial distress. If financial distress occurs at the end of a progressive process, then omitting time series of historical financial ratios from the analysis ignores the cumulative effect of previous financial ratios on the current consequences. This study incorporated the cumulative characteristics of financial distress by using the characteristics of a state space model that is able to perform long-term forecasts to dynamically predict an enterprise's financial distress. Kalman filtering is used to estimate the model parameters. Thus, the model constructed in this paper is a dynamic financial prediction model that has the benefit of forecasting over the long term. Additionally, current data are used to forecast the future annual financial position and to judge whether the establishment will be in financial distress.     

利用迭代滤波改进乘积高阶模糊函数

乘积高阶模糊函数(PHAF)是因分析mc-PPS而提出来的,但它抑制交叉项的能力有限,难以实现mc-PPS估计。该文提出了逐次滤除最强PHAF峰对应的分量来减少交叉项的迭代滤波方法,改进后的PHAF具有较好的鲁棒性:减少了估计盲区,并且具有更好的估计精度,降低了信噪比门限,而且能估计低阶相位系数,这些性能由多个mc-PPS仿真例子所验证。     

基于专题文献的信息内容过滤系统设计

针对当前综合性信息过滤系统不能满足不同知识结构、不同查询兴趣的专业人员对特定领域的信息获取的需求,在对经典信息过滤算法进行研究分析的基础上,提出了基于专题文献的信息内容过滤系统的设计,并加以实现。本系统在专业词汇、特征提取、特征加权等方面进行了改进,最后在小范围内进行了测试。     

基于遗传神经网络的股价波动预测

对价格、成交量特征样本序列分别进行小波包分解,将其小波包系数单支重构能量值共同作为样本波动的本质表征,结合遗传神经网络进行股票价格波动预测。对沪市股票上海汽车(600104)、宝钢股份(600019)进行实证研究,结果表明,该模型具有收敛速度快和预测精度高的特点。