Pattern‐Similarity‐Based Model for Time Series Prediction

This research proposes a pattern/shape‐similarity‐based clustering approach for time series prediction. This article uses single hidden Markov model (HMM) for clustering and combines it with soft computing techniques (fuzzy inference system/artificial neural network) for the prediction of time series. Instead of using distance function as an index of similarity, here shape/pattern of the sequence is used as the similarity index for clustering, which overcomes few of the shortcomings associated with distance‐based clustering approaches. Underlying hidden properties of time series are captured with the help of HMM. The prediction method used here exploits the pattern identification prowess of the HMM for cluster selection and the generalization and nonlinear modeling capabilities of soft computing methods to predict the output of the system. To see the validity of the proposed method in the real‐life scenario, it is tested on four different time series. The first is a benchmark Mackey–Glass time series, which is tested for delay parameters τ = 17 and τ = 30. The remaining time series are monthly sunspot data time series, Laser data time series and the last is Lorenz attractor time series. Simulation results show that the proposed method provide a better prediction performance in comparison with the existing methods.     

A logical foundation for the case‐based reasoning cycle

Case‐based reasoning (CBR) has drawn considerable attention in artificial intelligence (AI) fields with many successful applications in systems such as e‐commerce and multiagent systems. For the moment, research and development of CBR basically follows the traditional process model of CBR, i.e., the R⁴ model and problem space model introduced in 1994 and 1996, respectively. However, there has been no logical analysis for this popular CBR model. This article will fill this gap by providing a unified logical foundation for the CBR cycle. The proposed approach is based on an integration of traditional mathematical logic, fuzzy logic, and similarity‐based reasoning. At the same time, we examine the CBR cycle from the knowledge‐based (KB) viewpoint. The proposed logical approach can facilitate research and development of CBR. © 2003 Wiley Periodicals, Inc.     

基于卷积的时态CBR快速检索算法及应用*

探讨了如何增强CBR对一种常见的时态信息,即时间序列数据的检索能力;分析了已有的基于傅里叶频谱分析的时间序列检索算法应用于CBR时遇到的问题,并根据时态CBR检索的需要,提出了一种新的基于循环卷积和傅里叶变换时间序列检索算法.理论分析和数值实验结果都证明,提出的算法在检索效率上有一定的优势.将采取这种检索方法的时态CBR应用于时间序列的预测问题中,取得了较好的预测效果且具有较高的预测效率.     

An empirical study of predicting software faults with case-based reasoning

The resources allocated for software quality assurance and improvement have not increased with the ever-increasing need for better software quality. A targeted software quality inspection can detect faulty modules and reduce the number of faults occurring during operations. We present a software fault prediction modeling approach with case-based reasoning (CBR), a part of the computational intelligence field focusing on automated reasoning processes. A CBR system functions as a software fault prediction model by quantifying, for a module under development, the expected number of faults based on similar modules that were previously developed. Such a system is composed of a similarity function, the number of nearest neighbor cases used for fault prediction, and a solution algorithm. The selection of a particular similarity function and solution algorithm may affect the performance accuracy of a CBR-based software fault prediction system. This paper presents an empirical study investigating the effects of using three different similarity functions and two different solution algorithms on the prediction accuracy of our CBR system. The influence of varying the number of nearest neighbor cases on the performance accuracy is also explored. Moreover, the benefits of using metric-selection procedures for our CBR system is also evaluated. Case studies of a large legacy telecommunications system are used for our analysis. It is observed that the CBR system using the Mahalanobis distance similarity function and the inverse distance weighted solution algorithm yielded the best fault prediction. In addition, the CBR models have better performance than models based on multiple linear regression. Taghi M. Khoshgoftaar is a professor of the Department of Computer Science and Engineering, Florida Atlantic University and the Director of the Empirical Software Engineering Laboratory. His research interests are in software engineering, software metrics, software reliability and quality engineering, computational intelligence, computer performance evaluation, data mining, and statistical modeling. He has published more than 200 refereed papers in these areas. He has been a principal investigator and project leader in a number of projects with industry, government, and other research-sponsoring agencies. He is a member of the Association for Computing Machinery, the IEEE Computer Society, and IEEE Reliability Society. He served as the general chair of the 1999 International Symposium on Software Reliability Engineering (ISSRE’99), and the general chair of the 2001 International Conference on Engineering of Computer Based Systems. Also, he has served on technical program committees of various international conferences, symposia, and workshops. He has served as North American editor of the Software Quality Journal, and is on the editorial boards of the journals Empirical Software Engineering, Software Quality, and Fuzzy Systems. Naeem Seliya received the M.S. degree in Computer Science from Florida Atlantic University, Boca Raton, FL, USA, in 2001. He is currently a Ph.D. candidate in the Department of Computer Science and Engineering at Florida Atlantic University. His research interests include software engineering, computational intelligence, data mining, software measurement, software reliability and quality engineering, software architecture, computer data security, and network intrusion detection. He is a student member of the IEEE Computer Society and the Association for Computing Machinery.     

Flexible knowledge representation and new similarity measure: Application on case based reasoning for waste treatment

In Case Based Reasoning the representation of a case and the similarity measures are two difficult steps in the conception of a system. Often, these steps are developed to resolve one kind of problem. However, in some of them such as recovery treatment processes generation, it is necessary for the system to be able to modify and adapt the representation of a case and the similarity measures with respect of the context and also the kind of solutions proposed. In this paper, authors introduce a new method to represent cases with a flexibility based on a structure in a connectionist model. This flexibility is needed due to the complexity of cases, the number of possible options and to ensure the durability of the system. In a second main contribution, authors introduce a method for the selection of source cases using abstraction, conceptualisation and inference mechanisms. Finally, authors test their system in a CBR developed on SWI-Prolog with different problems. The CBR is applied to find new recovery processes and try to estimate the new upgraded product generated.     

Impact of momentum bias on forecasting through knowledge discovery techniques in the foreign exchange market

To an increasing extent since the late 1980s, software learning methods including neural networks (NN) and case based reasoning (CBR) have been used for prediction in financial markets and other areas. In the past, the prediction of foreign exchange rates has focused on isolated techniques, as exemplified by the use of time series models including regression models or smoothing methods to identify cycles and trends. At best, however, the use of isolated methods can only represent fragmented models of the causative agents, which underlie business cycles. Experience with artificial intelligence applications since the early 1980s points toward a multistrategy approach to discovery and prediction.This paper investigates the impact of momentum bias on forecasting financial markets through knowledge discovery techniques. Different modes of bias are used as input into learning systems using implicit knowledge representation (NNs) and CBR. The concepts are examined in the context of predicting movements in the Japanese yen.     

基于案例的备件需求预测技术及软件

运用基于案例的推理（CBR）原理，提出一种备件需求的预测技术，研究了相似识别、相似度，相似基准系统等问题。并设计、并发基于案例推理的备件需求预测软件系统。     

Similarity and metrics in case‐based reasoning

Similarity is a core concept in case‐based reasoning (CBR), because case base building, case retrieval, and even case adaptation all use similarity or similarity‐based reasoning. However, there is some confusion using similarity, similarity measures, and similarity metrics in CBR, in particular in domain‐dependent CBR systems. This article attempts to resolve this confusion by providing a unified framework for similarity, similarity relations, similarity measures, and similarity metrics, and their relationship. This article also extends some of the well‐known results in the theory of relations to similarity metrics. It appears that such extension may be of significance in case base building and case retrieval in CBR, as well as in various applied areas in which similarity plays an important role in system behavior. © 2002 Wiley Periodicals, Inc.     

Volumetric Billboards

We introduce an image‐based representation, called volumetric billboards, allowing for the real‐time rendering of semi‐transparent and visually complex objects arbitrarily distributed in a 3D scene. Our representation offers full parallax effect from any viewing direction and improved anti‐aliasing of distant objects. It correctly handles transparency between multiple and possibly overlapping objects without requiring any primitive sorting. Furthermore, volumetric billboards can be easily integrated into common rasterization‐based renderers, which allows for their concurrent use with polygonal models and standard rendering techniques such as shadow‐mapping. The representation is based on volumetric images of the objects and on a dedicated real‐time volume rendering algorithm that takes advantage of the GPU geometry shader. Our examples demonstrate the applicability of the method in many cases including levels‐of‐detail representation for multiple intersecting complex objects, volumetric textures, animated objects and construction of high‐resolution objects by assembling instances of low‐resolution volumetric billboards.     

Ontology-based similarity for product information retrieval

Product development of today is becoming increasingly knowledge intensive. Specifically, design teams face considerable challenges in making effective use of increasing amounts of information. In order to support product information retrieval and reuse, one approach is to use case-based reasoning (CBR) in which problems are solved “by using or adapting solutions to old problems.” In CBR, a case includes both a representation of the problem and a solution to that problem. Case-based reasoning uses similarity measures to identify cases which are more relevant to the problem to be solved. However, most non-numeric similarity measures are based on syntactic grounds, which often fail to produce good matches when confronted with the meaning associated to the words they compare. To overcome this limitation, ontologies can be used to produce similarity measures that are based on semantics. This paper presents an ontology-based approach that can determine the similarity between two classes using feature-based similarity measures that replace features with attributes. The proposed approach is evaluated against other existing similarities. Finally, the effectiveness of the proposed approach is illustrated with a case study on product–service–system design problems.     

Assessment of four modifications of a novel indexing technique for case‐based reasoning

In this article, we investigate four variations (D‐HS^M, D‐HS^W, D‐HS^E, and D‐HS^EW) of a novel indexing technique called D‐HS designed for use in case‐based reasoning (CBR) systems. All D‐HS modifications are based on a matrix of cases indexed by their discretized attribute values. The main differences between them are in their attribute discretization stratagem and similarity determination metric. D‐HS^M uses a fixed number of intervals and simple intersection as a similarity metric; D‐HS^W uses the same discretization approach and a weighted intersection; D‐HS^E uses information gain to define the intervals and simple intersection as similarity metric; D‐HS^EW is a combination of D‐HS^E and D‐HS^W. Benefits of using D‐HS include ease of case and similarity knowledge maintenance, simplicity, accuracy, and speed in comparison to conventional approaches widely used in CBR. We present results from the analysis of 20 case bases for classification problems and 15 case bases for regression problems. We demonstrate the improvements in accuracy and/or efficiency of each D‐HS modification in comparison to traditional k‐NN, R‐tree, C4,5, and M5 techniques and show it to be a very attractive approach for indexing case bases. We also illuminate potential areas for further improvement of the D‐HS approach. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 353–383, 2007.     

Hybridizing principles of TOPSIS with case-based reasoning for business failure prediction

Case-based reasoning (CBR) solves many real-world problems under the assumption that similar observations have similar outputs. As an implementation of this assumption and inspired by the technique for order performance by the similarity to ideal solution (TOPSIS), this paper proposes a new type of multiple criteria CBR method for binary business failure prediction (BFP) with similarities to positive and negative ideal cases (SPNIC). Assuming that the binary prediction of business failure generates two results, i.e., failure and non-failure, we set the principle of this CBR forecasting method which is termed as SPNIC-based CBR as follows: new observations should have the same output as the positive or negative ideal case to which they are more similar. From the perspective of CBR, the SPNIC-based CBR forecasting method consists of R⁴ processes: retrieving positive and negative ideal cases, reusing solutions of ideal cases to forecast, retain cases, and reconstruct the case base. As a demonstration, we applied this method to forecast business failure in China with three data representations of a formerly collected dataset from normal economic environment and a representation of a recently collected dataset from financial crisis environment. The results indicate that this new CBR forecasting method can produce significantly better short-term discriminate capability than comparative methods, except for support vector machine, in normal economic environment; On the contrary, it cannot produce acceptable performance in financial crisis environment. Further topics about this method are discussed.     

Networked‐based stabilization via discontinuous Lyapunov functionals

This paper presents a new stability and L₂‐gain analysis of linear Networked Control Systems (NCS). The new method is inspired by discontinuous Lyapunov functions that were introduced by Naghshtabrizitextitet al. (Syst. Control Lett. 2008; 57 :378–385; Proceedings 26th American Control Conference, New York, U.S.A., July 2007) in the framework of impulsive system representation. Most of the existing works on the stability of NCS (in the framework of time delay approach) are reduced to some Lyapunov‐based analysis of systems with uncertain and bounded time‐varying delays. This analysis via time‐independent Lyapunov functionals does not take advantage of the sawtooth evolution of the delays induced by sample‐and‐hold. The latter drawback was removed by Fridman (Automatica 2010; 46 :421–427), where time‐dependent Lyapunov functionals for sampled‐data systems were introduced. This led to essentially less conservative results. The objective of the present paper is to extend the time‐dependent Lyapunov functional approach to NCS, where variable sampling intervals, data packet dropouts, and variable network‐induced delays are taken into account. The Lyapunov functionals in this paper depend on time and on the upper bound of the network‐induced delay, and these functionals do not grow along the input update times. The new analysis is applied to the state‐feedback and to a novel network‐based static output‐feedback H_∞ control problems. Numerical examples show that the novel discontinuous terms in Lyapunov functionals essentially improve the results. Copyright © 2011 John Wiley & Sons, Ltd.     

Abductive case‐based reasoning

This article introduces abductive case‐based reasoning (CBR) and attempts to show that abductive CBR and deductive CBR can be integrated in clinical process and problem solving. Then it provides a unified formalization for integration of abduction, abductive CBR, deduction, and deductive CBR. This article also investigates abductive case retrieval and deductive case retrieval using similarity relations, fuzzy similarity relations, and similarity metrics. The proposed approach demonstrates that the integration of deductive CBR and abductive CBR is of practical significance in problem solving such as system diagnosis and analysis, and will facilitate research of abductive CBR and deductive CBR. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 957–983, 2005.     

Hybrid-biomarker case-based reasoning system for water pollution assessment in Abou Hammad Sharkia,Egypt

Water pollution by organic materials or metals is one of the problems that threaten humanity, both nowadays and over the next decades. Morphological changes in Nile Tilapia “Oreochromis niloticus” fish liver and gills can also represent the adaptation strategies to maintain some physiological functions or to assess acute and chronic exposure to chemicals found in water and sediments. This paper presents an automatic system for assessing water quality, in Sharkia Governorate – Egypt, based on microscopic images of fish gills and liver. The proposed system used fish gills and liver as hybrid-biomarker in order to detect water pollution. It utilized case-based reasoning (CBR) for indicating the degree of water quality based on the different histopathological changes in fish gills and liver microscopic images. Various performance evaluation metrics namely, retrieval accuracy, receiver operating characteristic (ROC) curves, F-measure, and G-mean have been used in order to objectively indicate the true performance of the system considering the unbalanced data. Experimental results showed that the proposed hybrid-biomarker CBR based system achieved water quality prediction accuracy of 97.9% using cosine distance similarity measure. Also, it outperformed both SVMs and LDA classifiers for the tested microscopic images dataset.     

New results on stability and L∞‐gain analysis for positive linear differential‐algebraic equations with unbounded time‐varying delays

This article addresses the problems of stability and L_∞‐gain analysis for positive linear differential‐algebraic equations with unbounded time‐varying delays for the first time. First, we consider the stability problem of a class of positive linear differential‐algebraic equations with unbounded time‐varying delays. A new method, which is based on the upper bounding of the state vector by a decreasing function, is presented to analyze the stability of the system. Then, by investigating the monotonicity of state trajectory, the L_∞‐gain for differential‐algebraic systems with unbounded time‐varying delay is characterized. It is shown that the L_∞‐gain for differential‐algebraic systems with unbounded time‐varying delay is also independent of the delays and fully determined by the system matrices. Two numerical examples are given to illustrate the obtained results.     

基于离均差的时间序列相似性度量

 时间序列的相似性度量是数据挖掘领域研究的一个热点,高维多元时间序列数据一般含有大量的噪声不利于相似性的比较。针对现有的时间序列度量方法存在的问题,在改进的时间序列自底向上融合算法的基础上,提出一种新的基于离均差的时间序列相似性度量的夹角余弦算法(Angle Cosine Metric Similarity,ACMS)。ACMS算法将时间序列等价为一个多维度的向量,充分考虑2个向量的方向和大小特征,增强振幅变化的鲁棒性,减少人为干扰,对数据挖掘中的聚类和预测具有帮助作用。     

一种支持时间序列数据的CBR检索算法

探讨了如何为CBR（基于范例的推理）增加对一种特殊的范例类型——时间序列数据的支持．分析了基于谱分析的时间序列相似度比较算法不适用于CBR检索的缺点，并在此基础上设计了一种综合性能很好的CBR检索算法．思路是把时间序列相似度比较转化成一个卷积问题，并用DFT来简化这个卷积的计算．通过对这种CBR检索算法进行了深入的理论分析和认真的实验，结果证明，提出的算法是一个高效的算法．在这个检索算法的基础上，CBR就能够席用到时序数据的分析推理中，具有广阔的应用前景．     

一种基于CBR的网络水产品价格预测方法

水产品价格的科学预测对水产业健康可持续发展具有重要作用。提出了一种基于案例推理CBR（Case—Based Reasoning）的水产品价格预测方法,其包括网络数据自动获取、基于概念树的面向属性归纳、案例的生成与表示、案例匹配及相似性计算、案例评价与修正等关键过程。在关键过程研究基础上实现了预测系统,并对网络水产品价格数据进行预测实验。结果表明,该系统能自动采集权威网站水产品价格数据,并能对水产品价格进行有效的分析与预测。     

Case-based retrieval of 3-dimensional shapes for the design of metal castings

This paper describes research into retrieval based on 3-dimensional shapes for use in the metal casting industry. The purpose of the system is to advise a casting engineer on the design aspects of a new casting by reference to similar castings which have been prototyped and tested in the past. The key aspects of the system are the orientation of the shape within the mould, the positions of feeders and chills, and particular advice concerning special problems and solutions, and possible redesign. The main focus of this research is the effectiveness of similarity measures based on 3-dimensional shapes. The approach adopted here is to construct similarity measures based on a graphical representation deriving from a shape decomposition used extensively by experienced casting design engineers. The paper explains the graphical representation and discusses similarity measures based on it. Performance measures for the CBR system are given, and the results for trials of the system are presented. The competence of the current case-base is discussed, with reference to a representation of cases as points in an n-dimensional feature space, and its principal components visualization. A refinement of the case base is performed as a result of the competence analysis and the performance of the case-base before and after refinement is compared.