Granger 相关性与时间序列预测

提出一种将Granger相关信息用于时间序列预测的方法,以解决时间序列预测过程中信息利用不完全的问题.首先,通过Granger相关性检验确定时间序列系统中的可利用信息;然后,利用神经网络将可利用信息抽取出来;最后,将抽取的可利用信息融入到时间序列的预测中.实验结果验证了所提出预测方法的有效性和稳定性.     

向量自回归模型Granger因果图的条件互信息辨识与应用

Grangerl因果性是衡量系统变量间动态关系的重要依据．传统的两变量Grangerl因果分析法容易产生伪因果关系,且不能刻画变量间的即时因果性．本文利用图模型方法研究时间序列变量间的Grangerl因果关系,建立了时间序列Granger因果图,提出Grangerl因果图的条件互信息辨识方法,利用混沌理论中的关联积分估计条件互信息,统计量的显著性由置换检验确定．仿真结果证实了方法的有效性,并利用该方法研究了空气污染指标以及中国股市间的Grangerl因果关系．     

非线性格兰杰因果关系在计算神经科学的应用

自从格兰杰1969年提出因果关系的概念之后,格兰杰因果关系的应用越来越广泛,但都是用来分析线性时间序列数据之间的内在联系。将线性格兰杰因果关系推广到非线性的情形,首先利用核函数的方法建立非线性时间序列模型,再按照线性格兰杰因果关系的基本思想定义非线性格兰杰因果关系,最后通过一个模拟的例子验证该方法的有效性。模拟数据的结果表明,该方法能有效地分析非线性数据之间的内在联系。     

矢量格兰杰因果关系及其在复杂网络中的应用

自从格兰杰1969年提出因果关系的概念之后,格兰杰因果关系在构造生物网络(基因网络、蛋白质网络、神经网络)的结构方面的应用越来越广泛,但是它只能用于研究单个节点和单个节点之间的内在联系。而实际的生物网络由于基因、神经元等的相互作用,往往呈现出非常复杂的网络结构,需要研究网络节点构成的组与组之间的内在联系。将格兰杰因果关系进行推广,得到矢量格兰杰因果关系的研究方法,并通过两个模拟的例子验证了方法的有效性。     

频域格兰杰因果关系及其在信号处理中的应用

自从格兰杰1969年提出因果关系的概念之后,格兰杰因果关系在信号处理、计算神经科学等许多领域的应用越来越广。人们可以利用格兰杰因果关系来分析多个变量之间的直接的相互作用,从而进一步研究各类变量之间的内在联系。以往都是在时域空间进行分析的,也就是说分析的对象都是时间序列数据,研究这些变量之间随着时间的变化是如何联系的。在时域空间的基础上,进一步从频域空间上对变量进行研究,分析在哪个频率段上变量之间存在相互作用,所得到的结论当然更具有意义。     

Root cause diagnosis of plant-wide oscillations using Granger causality

Oscillations are common in closed-loop controlled processes which, once generated, can propagate along process flows and feedback paths of the whole plant. It is important to detect and diagnose such oscillations to maintain high control performance. This paper presents a new data-driven time series method for diagnosing the sources and propagation paths of plant-wide oscillations. The proposed method first uses a latent variable method to select features which carry significant common oscillations, then applies both time-domain Granger causality and spectral Granger causality to provide reliable diagnosis of oscillation sources and propagations. Simulation tests and an industrial case study are shown to demonstrate the effectiveness of the proposed method.     

Causality analysis of neural connectivity: New tool and limitations of spectral Granger causality

Granger causality (GC) is one of the most popular measures to reveal causality influence of time series based on the estimated linear regression model and has been widely applied in economics and neuroscience due to its simplicity, understandability and easy implementation. Especially, its counterpart in frequency domain, spectral GC, has recently received growing attention to study causal interactions of neurophysiological data in different frequency ranges. In this paper, on the one hand, for one equality in the linear regression model (frequency domain) we point out that all items at the right-hand side of the equality make contributions (thus have causal influence) to the unique item at the left-hand side of the equality, and thus a reasonable definition for causality from one variable to another variable (i.e., the unique item) should be able to describe what percentage the variable occupies among all these contributions. Along this line, we propose a new spectral causality definition. On the other hand, we point out that spectral GC has its inherent limitations because of the use of the transfer function of the linear regression model and as a result may not reveal real causality at all and lead to misinterpretation result. By one example we demonstrate that the results of spectral GC analysis are misleading but the results from our definition are much reasonable. So, our new tool may have wide potential applications in neuroscience.     

基于相对熵的概率假设密度滤波器序贯蒙特卡罗实现方式

概率假设密度滤波器的典型序贯蒙特卡罗实现方式与粒子滤波类似,均是利用大量加权粒子估计多目标状态,典型实现方式是为每个期望目标分配固定数目的粒子,这导致较大的算法时间开销.鉴于此,建立了基于相对熵的序贯蒙特卡罗实现方式.首先计算两个不同规模粒子集合的相对熵,与预设阈值进行比较以确定粒子数目,从而动态调整粒子数目.仿真结果表明,所提出的实现方式提高了跟踪效率,在大部分时间步上优于典型实现方式.     

基于Kullback-Leibler分歧的变分滤波WSNs贝叶斯移动定位跟踪西

为了在降低资源能耗和带宽占用情况下,提高无线传感器网络WSNs移动目标定位跟踪的精度,提出了基于Kullback-Leibler分歧的变分滤波的WSNs贝叶斯移动目标定位跟踪算法。首先,利用高斯和Wishart分布在不考虑速度限制和方向移动限制情况下,构建WSNs移动定位的贝叶斯状态演化模型,并基于路径损耗模型构建移动目标定位的观测模型;其次,利用Kullback-Leibler分歧构建变分滤波的误差计算模型,通过周围激活节点实现移动节点目标的位置估计,设计了递归概率计算过程综合预测和更新两个过程,并实现了定位和目标跟踪的同步化;最后,通过仿真验证了所提模型在跟踪精度和资源节约上的优势。     

基于KL散度和近邻点间距离的球面嵌入算法

针对现有球面嵌入算法在非近邻点间的距离度量不准确或缺失的情况下,不能有效地进行低维嵌入的问题,提出了一种新的球面嵌入算法,它能够只利用近邻点间的距离,将任何尺度的高维数据嵌入到单位球面上,同时求出适合原始数据分布的球面半径。该算法从一个随机产生的球面分布开始,利用KL散度衡量每对近邻点间的归一化距离在原始空间和球面空间中的差异,并基于此差异构建出目标函数,然后再用带有动量的随机梯度下降法,不断优化球面上点的分布,直到结果稳定。为了测试算法,模拟产生了两类球面分布数据：分别是球面均匀分布和球面正态分布的数据。实验结果表明,对于球面均匀分布的数据,即使在近邻点个数很少的情况下,仍然能够将数据准确地嵌入球面空间,嵌入后的数据分布与原始数据分布的均方根误差（RMSE）低于0.00001,且球面半径的估算误差低于0.000001;而对于球面正态分布的数据,在近邻点个数较多的情况下,该算法也可以将数据较准确地嵌入球面空间。因此,在非近邻点间距离缺失的情况下,所提方法仍然可以较准确地对数据进行低维嵌入,这非常有利于数据的可视化研究。     

基于Kullback-Leibler距离的闭环系统传感器微小故障诊断

在闭环控制系统中,当故障幅值较小时,由故障带来的影响会被控制量所掩盖.因此,闭环系统中的微小故障诊断实现更为复杂.本文针对闭环系统中的传感器故障,提出了基于Kullback-Leibler(KL)距离的微小故障在线检测与估计方法.本文首先介绍了KL距离的定义及其在多变量故障检测中的应用,然后提出了结合KL距离与快速移动窗口主成分分析(MWPCA)的在线微小故障检测与估计模型.在高斯分布的假设下,利用系统输入输出残差构造MWPCA的数据矩阵,然后通过在线更新数据矩阵主成分的均值与方差实现KL距离的在线更新,最终实现闭环系统中传感器的在线故障检测与估计.仿真实验表明,该方法能有效实现具有低故障—噪声比(FNR)特性的微小故障诊断.     

Determination of ECoG information flow activity based on Granger causality and Hilbert transformation

Analysis of directional information flow patterns among different regions of the brain is important for investigating the relation between ECoG (electrocorticographic) and mental activity. The objective is to study and evaluate the information flow activity at different frequencies in the primary motor cortex. We employed Granger causality for capturing the future state of the propagation path and direction between recording electrode sites on the cerebral cortex. A grid covered the right motor cortex completely due to its size (approx. 8 cm × 8 cm) but grid area extends to the surrounding cortex areas. During the experiment, a subject was asked to imagine performing two activities: movement of the left small finger and/or movement of the tongue. The time series of the electrical brain activity was recorded during these trials using an 8 × 8 (0.016–300 Hz band with) ECoG platinum electrode grid, which was placed on the contralateral (right) motor cortex. For detection of information flow activity and communication frequencies among the electrodes, we have proposed a method based on following steps: (i) calculation of analytical time series such as amplitude and phase difference acquired from Hilbert transformation, (ii) selection of frequency having highest interdependence for the electrode pairs for the concerned time series over a sliding window in which we assumed time series were stationary, (iii) calculation of Granger causality values for each pair with selected frequency. The information flow (causal influence) activity and communication frequencies between the electrodes in grid were determined and shown successfully. It is supposed that information flow activity and communication frequencies between the electrodes in the grid are approximately the same for the same pattern. The successful employment of Granger causality and Hilbert transformation for the detection of the propagation path and direction of each component of ECoG among different sub-cortex areas were capable of determining the information flow (causal influence) activity and communication frequencies between the populations of neurons successfully.     

Simplified Granger causality map for data-driven root cause diagnosis of process disturbances

Root cause diagnosis is an important step in process monitoring, which aims to identify the sources of process disturbances. The primary challenge is that process disturbances propagate between different operating units because of the flow of material and information. Data-driven causality analysis techniques, such as Granger causality (GC) test, have been widely adopted to construct process causal maps for root cause diagnosis. However, the generated causal map is over-complicated and difficult to interpret because of the existence of process loops and the violation of statistical assumptions. In this work, a two-step procedure is proposed to solve this problem. First, a causal map is built by adopting the conditional GC analysis, which is viewed as a graph in the next step. In this graph, each vertex corresponds to a process variable under investigation, while the weight of the edge connecting two vertices is the F-value calculated by conditional GC. This graph is then simplified by computing its maximum spanning tree. Thus, the results of the causality analysis are transformed into a directed acyclic graph, which eliminates all loops, highlights the root cause variable, and facilitates the diagnosis. The feasibility of this method is illustrated with the application to the Tennessee Eastman benchmark process. In the investigated case studies, the proposed method outperforms the conditional GC test and provides an easy way to identify the root cause of process disturbances.     

Active contour driven by multi-scale local binary fitting and Kullback-Leibler divergence for image segmentation

Multimedia Tools and Applications - Image segmentation is an important processing in many applications such as image retrieval and computer vision. One of the most successful models for image...     

Spurious Granger causality between a broken-trend stationary process and a stochastic trend process

This paper examines spurious Granger causality between a trend stationary process with structural breaks and a stochastic trend process. Monte Carlo simulations show that whether or not there are deterministic variables in the testing models, the sample size and the parameter values of the data generation process can affect the empirical frequencies of spurious Granger causality relations in different degrees. The analysis also points out that an alternative rank-based causality test method can avoid the risk of spurious causality to some extent by adopting an intercept and deterministic trend term in the testing regressions.     

融合KL散度和移地距离的高斯混合模型相似性度量方法

为提高高斯混合模型(GMM)间相似性度量方法的计算效率和准确性,通过对称化KL散度(KLD)并结合移地距离(EMD)提出一种新的相似性度量方法。首先计算待比较的两个高斯混合模型内各高斯成分间的KL散度,对称化处理后用于构造地面距离矩阵;然后用线性规划方法求解两个高斯混合模型间的移地距离作为高斯混合模型间的相似性度量。实验结果表明,将该相似性度量方法应用于彩色图像检索,相对于传统方法能够提高检索的时间效率和准确性。     

‘Useful’ fuzzy measures of information,integrated ambiguity and directed divergence

In the present paper, a new concept of ‘useful’ fuzzy information, based on utility, is introduced by considering the uncertainties of fuzziness and probabilities of random events. A ‘useful’ fuzzy measure of integrated ambiguity is obtained by integration of fuzzy and probabilistic uncertainties with utility. A new ‘useful’ measure of fuzzy-directed divergence of a fuzzy set from another fuzzy set is proposed and its validity proved. Finally, the constrained optimisation of ‘useful’ fuzzy entropy and ‘useful’ fuzzy-directed divergence is studied.     

Evaluating code complexity triggers,use of complexity measures and the influence of code complexity on maintenance time

Code complexity has been studied intensively over the past decades because it is a quintessential characterizer of code’s internal quality. Previously, much emphasis has been put on creating code complexity measures and applying these measures in practical contexts. To date, most measures are created based on theoretical frameworks, which determine the expected properties that a code complexity measure should fulfil. Fulfilling the necessary properties, however, does not guarantee that the measure characterizes the code complexity that is experienced by software engineers. Subsequently, code complexity measures often turn out to provide rather superficial insights into code complexity. This paper supports the discipline of code complexity measurement by providing empirical insights into the code characteristics that trigger complexity, the use of code complexity measures in industry, and the influence of code complexity on maintenance time. Results of an online survey, conducted in seven companies and two universities with a total of 100 respondents, show that among several code characteristics, two substantially increase code complexity, which subsequently have a major influence on the maintenance time of code. Notably, existing code complexity measures are poorly used in industry.     

Parameterized complexity of the induced subgraph problem in directed graphs

In this Letter, we consider the parameterized complexity of the following problem: Given a hereditary property P on digraphs, an input digraph D and a positive integer k, does D have an induced subdigraph on k vertices with property P? We completely characterize hereditary properties for which this induced subgraph problem is W[1]-complete for two classes of directed graphs: general directed graphs and oriented graphs. We also characterize those properties for which the induced subgraph problem is W[1]-complete for general directed graphs but fixed parameter tractable for oriented graphs. These results are among the very few parameterized complexity results on directed graphs.     

Recursiveness, causality, and feedback

The various notions of recursiveness, causality, and feedback for stochastic processes are related and organized.