面向数据挖掘的时间序列聚类方法研究

一、引言自然界以及我们社会生活中的各种事物都在运动、变化和发展着,将它们按时间顺序记录下来,我们就可以得到各种各样的“时间序列”数据。对时间序列进行分析,可以揭示事物运动、变化和发展的内在规律,对于人们正确认识事物并据此作出科学的决策具有重要的现实意义。     

时间序列的细微距离发现

时间序列是信息系统一储存在的一类重要数据对象,而序列间的距离计算是很多时间序列数据开采或数据提取问题的核心。针对目前的序列距离定义模型对非总体的细微关联特征不敏感的问题,提出了一种新的时间序列距离定义模－时间序列的细微距离ＭＤ（Ｘ,Ｙ）,并提出了一种将时间序列由时域映射到频域,在频域中分离出不同的序列变化形式,以确定时间序列细微差别程度的算法－ＦＤＤ算法。ＦＤＤ算法具有较高的效率,且可以 作基准值     

Feedforward networks in financial predictions: the future that modifies the present

The main goal of this paper is to show how relatively minor modifications of well-known algorithms (in particular, back propagation) can dramatically increase the performance of an artificial neural network (ANN) for time series prediction. We denote our proposed sets of modifications as the 'self-momentum', 'Freud' and 'Jung' rules. In our opinion, they provide an example of an alternative approach to the design of learning strategies for ANNs, one that focuses on basic mathematical conceptualization rather than on formalism and demonstration. The complexity of actual prediction problems makes it necessary to experiment with modelling possibilities whose inherent mathematical properties are often not well understood yet. The problem of time series prediction in stock markets is a case in point. It is well known that asset price dynamics in financial markets are difficult to trace, let alone to predict with an operationally interesting degree of accuracy. We therefore take financial prediction as a meaningful test bed for the validation of our techniques. We discuss in some detail both the theoretical underpinnings of the technique and our case study about financial prediction, finding encouraging evidence that supports the theoretical and operational viability of our new ANN specifications. Ours is clearly only a preliminary step. Further developments of ANN architectures with more and more sophisticated 'learning to learn' characteristics are now under study and test.     

多维逆M序列及其在多变量系统辨识中的应用

基于Ｍ序列和逆Ｍ序列,提出了多维逆Ｍ序列发生器的结构形式。多维逆Ｍ序列近似周期白噪声,无直流分量,各维序列之间互不相关。言语中研究了多维逆Ｍ序列的性质,给出了自相关函数的计算公式。通过使用多维逆Ｍ序列,将多变量系统参数辨识简化为各通道对应的单变量系统参数辨识。多维逆Ｍ序列具有很好的推广应用价值。     

Efficient algorithms for segmentation of item-set time series

We propose a special type of time series, which we call an item-set time series, to facilitate the temporal analysis of software version histories, email logs, stock market data, etc. In an item-set time series, each observed data value is a set of discrete items. We formalize the concept of an item-set time series and present efficient algorithms for segmenting a given item-set time series. Segmentation of a time series partitions the time series into a sequence of segments where each segment is constructed by combining consecutive time points of the time series. Each segment is associated with an item set that is computed from the item sets of the time points in that segment, using a function which we call a measure function. We then define a concept called the segment difference, which measures the difference between the item set of a segment and the item sets of the time points in that segment. The segment difference values are required to construct an optimal segmentation of the time series. We describe novel and efficient algorithms to compute segment difference values for each of the measure functions described in the paper. We outline a dynamic programming based scheme to construct an optimal segmentation of the given item-set time series. We use the item-set time series segmentation techniques to analyze the temporal content of three different data sets–Enron email, stock market data, and a synthetic data set. The experimental results show that an optimal segmentation of item-set time series data captures much more temporal content than a segmentation constructed based on the number of time points in each segment, without examining the item set data at the time points, and can be used to analyze different types of temporal data.     

On Aperiodic and Star-Free Formal Power Series in Partially Commuting Variables

Formal power series over non-commuting variables have been investigated as representations of the behavior of automata with multiplicities. Here we introduce and investigate the concepts of aperiodic and of star-free formal power series over semirings and partially commuting variables. We prove that if the semiring K is idempotent and commutative, or if K is idempotent and the variables are non-commuting, then the product of any two aperiodic series is again aperiodic. We also show that if K is idempotent and the matrix monoids over K have a Burnside property (satisfied, e.g. by the tropical semiring), then the aperiodic and the star-free series coincide. This generalizes a classical result of Schützenberger (Inf. Control 4:245–270, 1961) for aperiodic regular languages and subsumes a result of Guaiana et al. (Theor. Comput. Sci. 97:301–311, 1992) on aperiodic trace languages. This work partly supported by the DAAD-PROCOPE project Temporal and Quantitative Analysis of Distributed Systems.     

Learning based brain emotional intelligence as a new aspect for development of an alarm system

The multi criteria and purposeful prediction approach has been introduced and is implemented by the fast and efficient behavioral based brain emotional learning method. On the other side, the emotional learning from brain model has shown good performance and is characterized by high generalization property. New approach is developed to deal with low computational and memory resources and can be used with the largest available data sets. The scope of paper is to reveal the advantages of emotional learning interpretations of brain as a purposeful forecasting system designed to warning; and to make a fair comparison between the successful neural (MLP) and neurofuzzy (ANFIS) approaches in their best structures and according to prediction accuracy, generalization, and computational complexity. The auroral electrojet (AE) index are used as practical examples of chaotic time series and introduced method used to make predictions and warning of geomagnetic disturbances and geomagnetic storms based on AE index.     

Temporal aggregation, systematic sampling, and the Hodrick-Prescott filter

The time aggregation properties of the Hodrick-Prescott (HP) filter, which decomposes a time series into trend and cycle, are analyzed for the case of annual, quarterly, and monthly data. Aggregation of the disaggregate components cannot be obtained as the exact result from direct application of an HP filter to the aggregate series. Employing several criteria, HP decompositions for different levels of aggregation that provide similar results can be found. The aggregation is guided by the principle that the period associated with the frequency for which the filter gain is should not be altered. This criterion is intuitive and easy to apply. It is shown that it is approximated, to the first order, by an already proposed empirical rule and that alternative, more complex criteria yield similar results. Moreover, the values of the smoothing parameter of the HP filter that provide results which are approximately consistent under aggregation are considerably robust with respect to the ARIMA model of the series. Aggregation is found to perform better for the case of temporal aggregation than for systematic sampling. The desirability of exact aggregation consistency is investigated. A clarification concerning the supposed spuriousness of the cycles obtained by the HP filter is discussed.     

Espaces métriques rationnellement présentés et complexité, le cas de l'espace des fonctions réelles uniformément continues sur un intervalle compact

We define the notion of rational presentation of a complete metric space, in order to study metric spaces from the algorithmic complexity point of view. In this setting, we study some representations of the space C[0,1] of uniformly continuous real functions over [0,1] with the usual norm: ||f||_∞ = Sup{|f(x)|; 0x1}. This allows us to have a comparison of global kind between complexity notions attached to these presentations. In particular, we get a generalization of Hoover's results concerning the Weierstrass approximation theorem in polynomial time. We get also a generalization of previous results on analytic functions which are computable in polynomial time.     

新型方波-直流电压信号的转换方法

围绕CCD(电荷耦合器件 )线阵在控制电路中的应用的难点 (如何将CCD输出的原始信号经过初步处理后所得的方波信号转换成直流电压信号 )进行了讨论和分析。首先介绍了两种传统的转换方法 ,并对这两种方法的优缺点进行了分析和比较。最后提出了一种新颖的转换方法 :基于滤波思想的方波 -直流电压信号的转换方法。经实验验证 ,这种方法所得的直流电压信号确实稳定、可靠