Cheung Hiu-Yan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 83-87). / Abstracts in English and Chinese. / ACKNOWLEGMENTS --- p.iii / LIST OF TABLES --- p.iv / LIST OF ILLUSTRATIONS --- p.vi / CHAPTER / Chapter ONE --- INTRODUCTION --- p.1 / Chapter TWO --- THE LITERATURE REVIEW --- p.6 / The Family of the ARFIMA Process / Parameter Estimation of the ARFIMA Process / Applications in Economic and Financial Time Series / Chapter THREE --- THEORETICAL MODELS AND METHODOLOGY --- p.16 / Theoretical Models of Long-memory Process / Parameter Estimation / Model Selection Criteria / Hypothesis Testing / Diagnostic Checking / Evaluating the Forecasting Performance / Chapter FOUR --- EMPIRICAL RESULTS OF SIMULATION EXPERIMENTS --- p.37 / Monte Carlo Simulation / Parameter Estimation / Results of Simulation Experiments / Chapter FIVE --- DATA AND EMPIRICAL RESULTS --- p.46 / Data Description / A Long-memory Model for the Return Series / Model Evaluation / Chapter SIX --- CONCLUSION --- p.55 / TABLES --- p.58 / ILLUSTRATIONS --- p.67 / APPENDICES --- p.79 / BIBLOGRAPHY --- p.83

Yau Po Chung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 91-92). / Abstract also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Background --- p.3 / Chapter 2.1 --- Temporal Theories --- p.3 / Chapter 2.2 --- Related Works --- p.4 / Chapter 2.2.1 --- Consistency and Satisfiability of Timing Specifications --- p.4 / Chapter 2.2.2 --- Symbolic Constraint Satisfaction --- p.5 / Chapter 3 --- Previous Developed Work --- p.7 / Chapter 3.1 --- Previous Problem Domain --- p.7 / Chapter 3.1.1 --- Basics of MC68000 Read Cycle --- p.7 / Chapter 3.2 --- Knowledge-based System Structure --- p.9 / Chapter 3.3 --- Diagnostic Reasoning Mechanisms --- p.10 / Chapter 3.4 --- Time Range Approach --- p.11 / Chapter 3.4.1 --- Time Range Representation --- p.11 / Chapter 3.4.2 --- Constraint Satisfaction of Time Ranges --- p.12 / Chapter 3.4.3 --- Constraint Propagation of Time Ranges --- p.13 / Chapter 3.5 --- Fuzzy Time Point Approach --- p.14 / Chapter 3.5.1 --- Fuzzy Time Point Models --- p.14 / Chapter 3.5.2 --- Definition of Fuzzy Time Points --- p.15 / Chapter 3.5.3 --- Constraint Propagation of Fuzzy Time Points --- p.17 / Chapter 3.5.4 --- Constraint Satisfaction of Fuzzy Time Points --- p.18 / Chapter 4 --- The Proposed Segmented Time Range Approach --- p.20 / Chapter 4.1 --- Introduction --- p.20 / Chapter 4.2 --- The Insufficiency of The Existing Time Range Approach --- p.22 / Chapter 4.3 --- Segmented Time Range Approach --- p.23 / Chapter 4.3.1 --- The Representation --- p.23 / Chapter 4.3.2 --- Constraint Propagation and Satisfaction --- p.25 / Chapter 4.3.3 --- Contributions --- p.25 / Chapter 4.3.4 --- Limitations --- p.29 / Chapter 4.4 --- Conclusion --- p.30 / Chapter 5 --- New Problem Domain and Our New System --- p.31 / Chapter 5.1 --- Introduction --- p.31 / Chapter 5.2 --- Pentium-SRAM Interfacing Problem --- p.31 / Chapter 5.2.1 --- Asynchronous SRAM Solution --- p.32 / Chapter 5.2.2 --- Synchronous SRAM Solution --- p.33 / Chapter 5.3 --- The Knowledge Base --- p.35 / Chapter 5.4 --- Characteristics of Our New System --- p.35 / Chapter 6 --- Burst Read Cycle --- p.37 / Chapter 6.1 --- Introduction --- p.37 / Chapter 6.2 --- Asynchronous SRAM Solution --- p.37 / Chapter 6.2.1 --- Implementation --- p.39 / Chapter 6.2.2 --- Implementation Results --- p.45 / Chapter 6.3 --- Synchronous SRAM Solution --- p.48 / Chapter 6.3.1 --- Implementation --- p.49 / Chapter 6.3.2 --- Implementation Results --- p.56 / Chapter 6.4 --- Conclusion --- p.58 / Chapter 7 --- Burst Write Cycle --- p.60 / Chapter 7.1 --- Introduction --- p.60 / Chapter 7.2 --- Asynchronous SRAM Solution --- p.60 / Chapter 7.2.1 --- Implementation --- p.61 / Chapter 7.2.2 --- Implementation Results --- p.67 / Chapter 7.3 --- Synchronous SRAM Solution --- p.71 / Chapter 7.3.1 --- Implementation --- p.71 / Chapter 7.3.2 --- Implementation Results --- p.79 / Chapter 7.4 --- Conclusion --- p.82 / Chapter 8 --- Conclusion --- p.83 / Chapter 8.1 --- Summary of Achievements --- p.83 / Chapter 8.2 --- Future Development --- p.86 / Appendix Some Characteristics of Our New System --- p.89 / Bibliography --- p.91

因果分析由于可以刻画随机事件之间的关系而被关注，而图模型则是描述因果关系的重要工具。在图模型框架中，数据集中隐含的因果关系被表示为定义在这个数据集上的贝叶斯网络，通过贝叶斯网络学习就可以完成数据集上的因果关系挖掘。因此,贝叶斯网络学习在因果分析中具有非常重要的作用。在本文中，我们提出了一种二段式的贝叶斯网络学习算法。在第一阶段，此算法从数据中构建出马尔可夫随机场。在第二阶段，此算法根据学习到的条件随机场构造出贝叶斯网络。本文中提出的二段式贝叶斯网络学习算法具有比现有算法更高的准确率，而且这种二段式算法中的一些技术可以很容易的被应用于其他贝叶斯网络学习算法当中。此外，通过与其他的时间序列中的因果分析模型（例如向量自回归和结构向量自回归模型）做比较，我们可以看出二段式的贝叶斯网络学习算法可以被用于时间序列的因果分析。 通过在真实数据集上的实验，我们证明的二段式贝叶斯网络学习算法在实际问题中的可用性。 / 本文开始介绍了基于约束的贝叶斯网络学习框架，其中的代表作是SGS 算法。在基于约束的贝叶斯网络学习框架中，如何减小测试条件独立的搜索空间是提高算法性能的关键步骤。二段式贝叶斯网络学习算法的核心即是研究如何减小条件独立测试的搜索空间。为此，我们证明了通过马尔可夫随机场来确定贝叶斯网络的结构可以有效的减小条件独立测试的计算复杂性以及增加算法的稳定性。在本文中，偏相关系数被用来度量条件独立。这种方法可用于基于约束的贝叶斯网络学习算法。具体来说，本文证明了在给定数据集的生成模型为线性的条件下，偏相关系数可被用于度量条件独立。而且本文还证明了高斯模型是线性结构方程模型的一个特例。本文比较了二段式的贝叶斯网络学习算法与当前性能最佳的贝叶斯算法在一系列真实贝叶斯网络上的表现。 / 文章的最后一部分研究了二段式的贝叶斯网络学习算法在时间序列因果分析中的应用。在这部分工作中，我们首先证明了结构向量自回归模型模型在高斯过程中不能发现同时期的因果关系。失败的原因是结构向量自回归模型不能满足贝叶斯网络的忠实性条件。因此，本文的最后一部分提出了一种区别于现有工作的基于贝叶斯网络的向量自回归和结构向量自回归模型学习算法。并且通过实验证明的算法在实际问题中的可用性。 / Causal analysis has drawn a lot of attention because it provides with deep insight of relations between random events. Graphical model is a dominant tool to represent causal relations. Under graphical model framework, causal relations implied in a data set are captured by a Bayesian network defined on this data set and causal discovery is achieved by constructing a Bayesian network from the data set. Therefore, Bayesian network learning plays an important role in causal relation discovery. In this thesis, we develop a Two-Phase Bayesian network learning algorithm that learns Bayesian network from data. Phase one of the algorithm learns Markov random fields from data, and phase two constructs Bayesian networks based on Markov random fields obtained. We show that the Two-Phase algorithm provides state-of-the-art accuracy, and the techniques proposed in this work can be easily adopted by other Bayesian network learning algorithms. Furthermore, we present that Two-Phase algorithm can be used for time series analysis by evaluating it against a series of time series causal learning algorithms, including VAR and SVAR. Its practical applicability is also demonstrated through empirical evaluation on real world data set. / We start by presenting a constraint-based Bayesian network learning framework that is a generalization of SGS algorithm [86]. We show that the key step in making Bayesian networks to learn efficiently is restricting the search space of conditioning sets. This leads to the core of this thesis: Two-Phase Bayesian network learning algorithm. Here we show that by learning Bayesian networks fromMarkov random fields, we efficiently reduce the computational complexity and enhance the reliability of the algorithm. Besides the proposal of this Bayesian network learning algorithm, we use zero partial correlation as an indicator of conditional independence. We show that partial correlation can be applied to arbitrary distributions given that data are generated by linear models. In addition, we prove that Gaussian distribution is a special case of linear structure equation model. We then compare our Two-Phase algorithm to other state-of-the-art Bayesian network algorithms on several real world Bayesian networks that are used as benchmark by many related works. / Having built an efficient and accurate Bayesian network learning algorithm, we then apply the algorithm for causal relation discovering on time series. First we show that SVAR model is incapable of identifying contemporaneous causal orders for Gaussian process because it fails to discover the structures faithful to the underlying distributions. We also develop a framework to learn true SVAR and VAR using Bayesian network, which is distinct from existing works. Finally, we show its applicability to a real world problem. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Wang, Zhenxing. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 184-195). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese. / Abstract --- p.i / Acknowledgement --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Causal Relation and Directed Graphical Model --- p.1 / Chapter 1.2 --- A Brief History of Bayesian Network Learning --- p.3 / Chapter 1.3 --- Some Important Issues for Causal BayesianNetwork Learning --- p.5 / Chapter 1.3.1 --- Learning Bayesian network locally --- p.6 / Chapter 1.3.2 --- Conditional independence test --- p.7 / Chapter 1.3.3 --- Causation discovery for time series --- p.8 / Chapter 1.4 --- Road Map of the Thesis --- p.10 / Chapter 1.5 --- Summary of the Remaining Chapters --- p.12 / Chapter 2 --- Background Study --- p.14 / Chapter 2.1 --- Notations --- p.14 / Chapter 2.2 --- Formal Preliminaries --- p.15 / Chapter 2.3 --- Constraint-Based Bayesian Network Learning --- p.24 / Chapter 3 --- Two-Phase Bayesian Network Learning --- p.33 / Chapter 3.1 --- Two-Phase Bayesian Network Learning Algorithm --- p.35 / Chapter 3.1.1 --- Basic Two-Phase algorithm --- p.37 / Chapter 3.1.2 --- Two-Phase algorithm with Markov blanket information --- p.59 / Chapter 3.2 --- Correctness Proof and Complexity Analysis --- p.73 / Chapter 3.2.1 --- Correctness proof --- p.73 / Chapter 3.2.2 --- Complexity analysis --- p.81 / Chapter 3.3 --- Related Works --- p.83 / Chapter 3.3.1 --- Search-and-score algorithms --- p.84 / Chapter 3.3.2 --- Constraint-based algorithms --- p.85 / Chapter 3.3.3 --- Other algorithms --- p.86 / Chapter 4 --- Measuring Conditional Independence --- p.88 / Chapter 4.1 --- Formal Definition of Conditional Independence --- p.88 / Chapter 4.2 --- Measuring Conditional Independence --- p.96 / Chapter 4.2.1 --- Measuring independence with partial correlation --- p.96 / Chapter 4.2.2 --- Measuring independence with mutual information --- p.104 / Chapter 4.3 --- Non-Gaussian Distributions and Equivalent Class --- p.108 / Chapter 4.4 --- Heuristic CI Tests UnderMonotone Faithfulness Condition --- p.116 / Chapter 5 --- Empirical Results of Two-Phase Algorithms --- p.125 / Chapter 5.1 --- Experimental Setup --- p.126 / Chapter 5.2 --- Structure Error After Each Phase of Two-Phase Algorithms --- p.129 / Chapter 5.3 --- Maximal and Average Sizes of Conditioning Sets --- p.131 / Chapter 5.4 --- Comparison of the Number of CI Tests Required by Dependency Analysis Approaches --- p.133 / Chapter 5.5 --- Reason forWhich Number of CI Tests Required Grow with Sample Size --- p.135 / Chapter 5.6 --- Two-Phase Algorithms on Linear Gaussian Data --- p.136 / Chapter 5.7 --- Two-phase Algorithms on Linear Non-Gaussian Data --- p.139 / Chapter 5.8 --- Compare Two-phase Algorithms with Search-and-Score Algorithms and Lasso Regression --- p.142 / Chapter 6 --- Causal Mining in Time Series Data --- p.146 / Chapter 6.1 --- A Brief Review of Causation Discovery in Time Series --- p.146 / Chapter 6.2 --- Limitations of Constructing SVAR from VAR --- p.150 / Chapter 6.3 --- SVAR Being Incapability of Identifying Contemporaneous Causal Order for Gaussian Process --- p.152 / Chapter 6.4 --- Estimating the SVARs by Bayesian Network Learning Algorithm --- p.157 / Chapter 6.4.1 --- Represent SVARs by Bayesian networks --- p.158 / Chapter 6.4.2 --- Getting back SVARs and VARs fromBayesian networks --- p.159 / Chapter 6.5 --- Experimental Results --- p.162 / Chapter 6.5.1 --- Experiment on artificial data --- p.162 / Chapter 6.5.2 --- Application in finance --- p.172 / Chapter 6.6 --- Comparison with Related Works --- p.174 / Chapter 7 --- Concluding Remarks --- p.178 / Bibliography --- p.184

"While much work has been done in mining nominal sequential data much less has been done on mining numeric time series data. This stems primarily from the problems of relating numeric data, which likely contains error or other variations which make directly relating values difficult. To handle this problem, many algorithms first convert data into a sequence of events. In some cases these events are known a priori, but in others they are not. Our work evaluates a set of time series data instances in order to determine likely candidates for unknown underlying events. We use the concept of bounding envelopes to represent the area around a numeric time series in which the unknown noise-free points could exist. We then use an algorithm similar to Apriori to build up sets of envelope intersections. The areas created by these intersections represent common patterns found throughout the data."

Graphs that evolve over time are called temporal graphs. They can be used to describe and represent real-world networks, including transportation networks, social networks, and communication networks, with higher fidelity and accuracy. However, research is still limited on how to manage large scale temporal graphs and execute queries over these graphs efficiently and effectively. This thesis investigates the problems of temporal graph data management related to node and edge sequence queries. In temporal graphs, nodes and edges can evolve over time. Therefore, sequence queries on nodes and edges can be key components in managing temporal graphs. In this thesis, the node sequence query decomposes into two parts: graph node similarity and subsequence matching. For node similarity, this thesis proposes a modified tree edit distance that is metric and polynomially computable and has a natural, intuitive interpretation. Note that the proposed node similarity works even for inter-graph nodes and therefore can be used for graph de-anonymization, network transfer learning, and cross-network mining, among other tasks. The subsequence matching query proposed in this thesis is a framework that can be adopted to index generic sequence and time-series data, including trajectory data and even DNA sequences for subsequence retrieval. For edge sequence queries, this thesis proposes an efficient storage and optimized indexing technique that allows for efficient retrieval of temporal subgraphs that satisfy certain temporal predicates. For this problem, this thesis develops a lightweight data management engine prototype that can support time-sensitive temporal graph analytics efficiently even on a single PC.

Thesis. 1978. M.S.--Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND AERONAUTICS. / Includes bibliographical references. / M.S.

Thesis. 1978. Ph.D.--Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Includes bibliographical references. / Ph.D.

A taxa de câmbio real dólar é formada em mercados à vista e futuro, operados necessariamente por intermédio de instituições financeiras que lucram tanto na corretagem quanto na posição proprietária de contratos. Essa característica cria um risco moral sobre os interesses de hedgers e intermediadores. Este trabalho investiga através do modelo econométrico do Vetor Autorregressivo VAR, testes de Causalidade e Função Resposta ao Impulso se é possível constatar empiricamente que as instituições financeiras, atuando em conjunto, podem influenciar significativamente a taxa de câmbio real/dólar. As conclusões apontam que o valor dos contratos futuros de dólar exerce grande influência sobre a taxa de câmbio à vista, o que implica em uma necessidade de supervisão constante da autoridade regulador, para coibir possíveis práticas de manipulação. / The Brazilian Real / US Dollar exchange rate is formed both on spot and future markets, operated necessarily by financial institutions; organizations profit both with intermediation and proprietary positions of contracts. Such characteristic incurs a moral hazard concerning hedgers and financial institutions interests. This research uses econometric models such as VAR, causality tests and impulse response function to investigate if financial institutions acting together are able to significantly influence the Real / Dollar exchange rate. The results show that the future exchange rate exercises great influence on the spot rate, reaffirming the necessity of constant supervision on financial institutions by the regulatory authority in order to restrain market manipulation.

This thesis covers four topics: i) Measuring dependence in time series through distance covariance; ii) Testing goodness-of-fit of time series models; iii) Threshold selection for multivariate heavy-tailed data; and iv) Inference for linear preferential attachment networks. Topic i) studies a dependence measure based on characteristic functions, called distance covariance, in time series settings. Distance covariance recently gathered popularity for its ability to detect nonlinear dependence. In particular, we characterize a general family of such dependence measures and use them to measure lagged serial and cross dependence in stationary time series. Assuming strong mixing, we establish the relevant asymptotic theory for the sample auto- and cross- distance correlation functions. Topic ii) proposes a goodness-of-fit test for general classes of time series model by applying the auto-distance covariance function (ADCV) to the fitted residuals. Under the correct model assumption, the limit distribution for the ADCV of the residuals differs from that of an i.i.d. sequence by a correction term. This adjustment has essentially the same form regardless of the model specification. Topic iii) considers data in the multivariate regular varying setting where the radial part $R$ is asymptotically independent of the angular part $\Theta$ as $R$ goes to infinity. The goal is to estimate the limiting distribution of $\Theta$ given $R\to\infty$, which characterizes the tail dependence of the data. A typical strategy is to look at the angular components of the data for which the radial parts exceed some threshold. We propose an algorithm to select the threshold based on distance covariance statistics and a subsampling scheme. Topic iv) investigates inference questions related to the linear preferential attachment model for network data. Preferential attachment is an appealing mechanism based on the intuition “the rich get richer” and produces the well-observed power-law behavior in net- works. We provide methods for fitting such a model under two data scenarios, when the network formation is given, and when only a single-time snapshot of the network is observed.

Os avanços na Tecnologia da Informação e a proliferação de itens de consumo, entre outros aspectos, mudaram o cenário e o desempenho das previsões. Os processos de previsão devem ser reexaminados, estabelecendo mecanismos de comunicação formais que compartilhem a informação entre os diferentes níveis hierárquicos dentro da organização, eliminando ou reduzindo o desconforto das previsões paralelas e desconexas oriundas de níveis hierárquicos diferentes. O objetivo deste trabalho é propor um sistema de apoio à decisão baseado em métodos matemáticos e sistemas de informação, capaz de integrar as previsões de vários níveis hierárquicos de uma empresa por um repositório de dados (Data Warehouse ou DW) e um Sistema de Apoio à Decisão (SAD) com sistema Business Intelligence (BI), onde os níveis hierárquicos acessem as informações com o nível de detalhe apropriado dentro do processo de decisão, alinhado às expectativas corporativas de crescimento. Assim, a modelagem realizada neste trabalho teve como foco a geração de cenários para criar um sistema de apoio à decisão, prevendo demandas agregadas e individuais, gerando uma estrutura de integração entre as previsões feitas em diferentes níveis e alinhando valores oriundos de métodos quantitativos e julgamento humano. Uma das maiores preocupações foi verificar qual método (séries temporais, métodos causais) teria destaque em um processo integrado de previsão. Entre os diferentes testes efetuados, pode-se destacar os seguintes resultados: (1) a suavização exponencial tripla proporcionou melhor ajuste (dos dados passados) de séries históricas de demandas mais agregadas e proporcionou previsões mais precisas de representatividades agregadas. Para séries históricas de demanda individual e representatividade individual, os outros métodos comparados apresentaram desempenho muito próximo; (2) a criação de diferentes cenários de previsão, fazendo uso de um repositório de dados e sistema de apoio à decisão, permitiu análise de uma gama de diferentes valores futuros. Uma forma de simulação para apoiar a formulação das expectativas da diretoria foi adaptada da literatura e sugerida; (3) os erros de previsão nas abordagens top-down ou bottom-up são estatisticamente iguais no contexto desta pesquisa. Conclui-se que o método de suavização exponencial tripla traz menos erros às previsões de séries mais agregadas, se comparado com outros métodos abordados no trabalho. Esse fato está de acordo com asserções encontradas na literatura pesquisada de que o método de suavização exponencial é cada vez mais utilizado na previsão, em detrimento dos métodos causais como a regressão múltipla. Conclui-se, principalmente, que os sistemas SAD e BI propostos deram suporte aos vários níveis hierárquicos, proporcionando variedades de estilos de decisão e que podem diminuir o hiato entre o raciocínio qualitativo adotado em nível estratégico e o aspecto quantitativo mais comum em níveis operacionais em qualquer empresa. / Advances in Information Technology (IT), and the increase of consumption items, among other things, changed the performance in the forecasts predictions. It is not uncommon that organizations will perform parallel forecasts within the various hierarchical levels without communicating with each other. The objective of this work is to build an integrated \"infrastructure\" for forecasting through a repository of data (Data Warehouse or DW) and a Decision Support System (DSS) with Business Intelligence (BI) where the hierarchical levels have access to the information with the appropriate level of detail within the process, aligned to the corporate growth expectations. The modeling in this work focused in the generation of scenarios to create a decision support system, predicting individual and aggregate demand, create a structure for integrating and aligning the estimated forecast generated by quantitative and qualitative methods. After a series of experimental tests, main results found were: (1) triple exponential smoothing provided the best fit using historical aggregated demand, and provided a more precise estimate of aggregate representation. For historical series of individual demand and individual representation, the other methods used for comparison performed similarly; (2) the creation of different scenarios for prediction, using data repository and decision support system, allowed for analysis of a range of different future values. The simulation to support management expectations has been adapted from the literature; (3) the prediction errors in the top-down and bottom-up approaches are statistically the same in the context of this research. In conclusion, the method of triple exponential smoothing has fewer errors in the forecasts of aggregated series when compared to other methods discussed in this work. Moreover, the DSS and BI systems provided decision-making support to the various hierarchical levels, reducing the gap between qualitative and quantitative decision processes thus bridging the strategic and operational decision making processes.