中国国防费时间序列预测模型的建立

时间序列模型(ARMA)是一种精度较高的短期预测模型.本文综合运用B-J时间序列建模方法,对中国国防费时间序列平稳性进行了判别;利用单位根方法检验了时间序列的单整阶数;利用自相关函数和偏自相关函数判别了时间序列模型的自回归阶数(AR(p))和移动平均阶数(MA(q));最后利用Eviews统计软件建立了合适的中国国防费时间序列模型,并进行了分析和预测.     

基于特征提取的金融时间序列形态挖掘

以金融时间序列的上升三角形形态为例给出了金融时间序列挖掘与特征提取的方法,在挖掘过程中充分利用挖掘者的经验背景知识,提出了一种基于特征提取的金融时间序列形态挖掘算法。该算法首先对金融时间序列进行趋势化预处理,将曲线转化为折线表示,形成形态的趋势片断,然后再从这些趋势片断中提取出相关形态的属性特征。它提高了序列形态特征提取的有效性,使搜索空间大为减小。     

时间序列数据流直方图构造方法研究

文章文针对金融等领域的时间序列数据流,提出了一种直方图的构造方法,该方法具有联机处理高频时间序列数据流的能力,并具有与最优化直方图构造方法接近的精度.     

时间序列相似性的图形相似方法研究

本文给出用欧氏距离与时间弯曲距离进行时间序列相似性判断的缺陷,并给出基于欧氏几何图形相似理论的判定两个时间序列相似性的方法。文中给出两条折线的相似性的判断方法。又由于时间序列与折线之间的可转化性,就把判断两折线的相似性方法运用到判定两个时间序列的相似性上。最后,把这种方法应用到聚类分析中,取得较好的效果。     

金融市场超高频时间序列ACD-GARCH-V模型研究

金融市场超高频时间序列建模是目前金融计量经济学研究的热点。该文在已有研究的基础上,建立了ACD-GARCH-V模型,通过实证分析考察了超高频交易量变化率及交易持续期对金融产品收益率和波动性的影响。     

贝叶斯时间序列方法研究与应用评述

文章对贝叶斯时间序列方法的研究与应用现状进行了评迷,内容包括一元时间序列、多元时间序列及模型识别等三个方面,以期为该方面的研究与应用者提供参考.     

居民储蓄存款余额的时间序列分析

本文以发自2006年的中国资本市场迅速上涨为背景,通过建立ARIMA模型对于我国备受关注的高额居民储蓄存款余额的变化进行了短期预测,阐述了储蓄存款余额所表现出的变化规律。     

集装箱吞吐量时间序列预测模型

改革开放给我国航运企业带来了机遇与挑战.在激烈的市场竞争中,航运企业必须进行科学的投资决策,合理的资源配置,以及有效的宏观管理,才能把握机遇赢得发展.对运量,运价的有效预测是进行科学决策的基本依据.笔者在运用了多种预测方法对我国某港口集装箱吞吐量历史数据建立模型以后发现,时间序列模型在处理同时具有趋势性和周期性规律的动态数据上具有独到之处.根据叠合模型的拟合结果来看,具有较好的预测效果.     

居民消费价格指数的时间序列分析及预测

时间序列是按照时间顺序取得的一系列数据,大多数的经济时间序列存在惯性,通过这种惯性分析可以由时间序列的历史数值对未来值进行预测.文章主要利用时间序列的趋势外推方法对我国目前居民消费价格指数(CPI)进行了建模析和预测,以达到合理预期和分析的目的.     

On the Detection of Contemporaneous Relationships Among Multiple Time Series

A study is carried out of a sampling from a half-normal and exponential distributions to develop a test of hypothesis on the mean. Although these distributions are similar, the corresponding uniformly most paerful test statistics are different. The exact distributions of these statistics my be written in terms of the incomplete gamma function. If the experimental data my be fitted by either distributions, it is advisable to carryout the test based on the half-normal distribution as it is generally more powerful than the one based on the exponential one.     

First and Second Derivatives in Time Series Classification Using DTW

In our previous work, we developed a new distance function based on a derivative and showed that our algorithm is effective. In contrast to well-known measures from the literature, our approach considers the general shape of a time series rather than standard distance of function (value) comparison. The new distance was used in classification with the nearest neighbor rule. Now we improve on our previous technique by adding the second derivative. In order to provide a comprehensive comparison, we conducted a set of experiments, testing effectiveness on 47 time series datasets from a wide variety of application domains. Our experiments show that this new method provides a significantly more accurate classification on the examined datasets.     

Time Series Classification Based on Spectral Analysis

For time series data with obvious periodicity (e.g., electric motor systems and cardiac monitor) or vague periodicity (e.g., earthquake and explosion, speech, and stock data), frequency-based techniques using the spectral analysis can usually capture the features of the series. By this approach, we are able not only to reduce the data dimensions into frequency domain but also utilize these frequencies by general classification methods such as linear discriminant analysis (LDA) and k-nearest-neighbor (KNN) to classify the time series. This is a combination of two classical approaches. However, there is a difficulty in using LDA and KNN in frequency domain due to excessive dimensions of data. We overcome the obstacle by using Singular Value Decomposition to select essential frequencies. Two data sets are used to illustrate our approach. The classification error rates of our simple approach are comparable to those of several more complicated methods.     

Diagnostic Checking of Unobserved-Components Time Series Models

Diagnostic checking of the specification of time series models is normally carried out using the innovations—that is, the one-step-ahead prediction errors. In an unobserved-components model, other sets of residuals are available. These auxiliary residuals are estimators of the disturbances associated with the unobserved components. They can often yield information that is less apparent from the innovations, but they suffer from the disadvantage that they are serially correlated even in a correctly specified model with known parameters. This article shows how the properties of the auxiliary residuals may be obtained, how they are related to each other and to the innovations, and how they can be used to construct test statistics. Applications are presented showing how residuals can be used to detect and distinguish between outliers and structural change.     

Some Properties and Generalizations of Non-negative Bayesian Time Series Models

We study the most basic Bayesian forecasting model for exponential family time series, the power steady model (PSM) of Smith, in terms of observable properties of one-step forecast distributions and sample paths. The PSM implies a constraint between location and spread of the forecast distribution. Including a scale parameter in the models does not always give an exact solution free of this problem, but it does suggest how to define related models free of the constraint. We define such a class of models which contains the PSM. We concentrate on the case where observations are non-negative. Probability theory and simulation show that under very mild conditions almost all sample paths of these models converge to some constant, making them unsuitable for modelling in many situations. The results apply more generally to non-negative models defined in terms of exponentially weighted moving averages. We use these and related results to motivate, define and apply very simple models based on directly specifying the forecast distributions.     

Smoothing Time Series with Local Polynomial Regression on Time

Time series smoothers estimate the level of a time series at time t as its conditional expectation given present, past and future observations, with the smoothed value depending on the estimated time series model. Alternatively, local polynomial regressions on time can be used to estimate the level, with the implied smoothed value depending on the weight function and the bandwidth in the local linear least squares fit. In this article we compare the two smoothing approaches and describe their similarities. Through simulations, we assess the increase in the mean square error that results when approximating the estimated optimal time series smoother with the local regression estimate of the level.     

Detecting Directionality in Time Series

Abstract

Directionality can be seen in many stationary time series from various disciplines, but it is overlooked when fitting linear models with Gaussian errors. Moreover, we cannot rely on distinguishing directionality by comparing a plot of a time series in time order with a plot in reverse time order. In general, a statistical measure is required to detect and quantify directionality. There are several quite different qualitative forms of directionality, and we distinguish: rapid rises followed by slow recessions; rapid increases and rapid decreases from the mean followed by slow recovery toward the mean; directionality above or below some threshold; and intermittent directionality. The first objective is to develop a suite of statistical measures that will detect directionality and help classify its nature. The second objective is to demonstrate the potential benefits of detecting directionality. We consider applications from business, environmental science, finance, and medicine. Time series data are collected from many processes, both natural and anthropogenic, by a wide range of organizations, and directionality can easily be monitored as part of routine analysis. We suggest that doing so may provide new insights to the processes.     

基于Copula函数的金融时间序列模型述评

结合当前Copula函数及其应用的热点问题,着重评述了基于Copula函数的金融时间序列模型的应用。鉴于利用Copula可以将边际分布和变量间的相依结构分开来研究这一优良性质,在设定和估计模型时便显得极为方便和灵活。从模型的构造、Copula函数的选择、模型的估计以及拟合优度检验等几方面展开阐述和评价,介绍了Copula模型在金融领域中的几类应用,并对Copula理论和应用的新视角进行了展望。     

基于稳健Cook距离的时间序列异常值诊断

Cook距离公式常用于回归模型的异常值诊断,但由于公式中的样本方差■对异常值敏感,导致公式缺乏稳健性,使得诊断效果不理想。基于以上问题,文章选取绝对离差中位数作为样本标准差的稳健估计量,得到了样本方差■的稳健估计量,进而构造出稳健Cook距离公式;借鉴传统Cook距离的回归模型异常值诊断理论,将稳健Cook距离公式应用于时间序列异常值诊断,拓展了传统Cook距离公式的异常值诊断领域。通过选取模拟样本量分别为50、100、200,污染率分别为0、1%、5%、10%的ARMA(1,1)序列及金融时间序列进行实例分析,结果发现:(1)在无污染时,稳健Cook距离法与常规Cook距离法的诊断正确率均为100%,两者没有出现"误诊"现象;(2)在样本量、污染率同时增大时,常规Cook距离诊断正确率急剧下降,当污染率达到5%及以上时,已基本无诊断力,而稳健Cook距离法依然能保持较高的诊断力。稳健Cook距离法不仅能应用于时间序列异常值诊断,也能应用于回归分析的异常值诊断。