No description available.

Estudos mostraram que o tráfego nas redes de dados tanto locais quanto de grande área, possui propriedades fractais como dependência de longa duração - Long-Range Dependence (LRD) e auto-similaridade. Devido à heterogeneidade de aplicações nessas redes, os traces de tráfego podem apresentar dependência de longa duração - Long Range Dependence (LRD), dependência de curta duração - Short Range Dependence (SRD) ou uma mistura de LRD com SRD. Sendo assim, este trabalho tem como objetivo sintetizar séries temporais gaussianas com flexibilidade de processamento no plano tempo-frequência a serem inseridas num gerador de tráfego com as características estatísticas específicas do tráfego encontrado em redes por comutação de pacotes reais, como autossimilaridade, LRD e SRD. Para isto foram desenvolvidos dois métodos para síntese de séries temporais gaussianas com LRD e simultânea introdução de SRD em diferentes faixas de frequência: Discrete Wavelet Tansform (DWT) com mapa de variâncias e Discrete Wavelet Packet Tansform (DWPT). Estes métodos utilizaram o mapa de variâncias cujo conceito foi desenvolvido neste trabalho. A validação dos métodos foi feita através de análise estatística e comparação com resultados de séries geradas pelo método Discrete Wavelet Transfom (DWT) de Backar utilizado em [1]. Além disso, também foi validada a ideia de que a DWPT é mais interessante que a DWT por ser mais flexível e prover uma maior flexibilidade de processamento no plano tempo-frequência. / Studies demonstrated that the data network traffic of Local Area Network (LAN) and Wide Area Network has fractal properties as long range dependence (LRD) and self-similarity. The traffic traces can show long range dependence, short range dependence or the both behaviors because of applications heterogeneity in these networks. This work objective is to synthetisize gaussian time series with processor flexibility in the time-frequency plan to be inserted in a traffic generator with the specific statistical traffic characteristics of real packet networks such as selfsimilarity, long range dependence (LRD) and short range dependence (SRD). Two methods were developed for the gaussian time series with LRD and SRD synthesis: Discrete Wavelet Tansform (DWT) with variance map and Discrete Wavelet Packet Tansform (DWPT). These methods used the variance map which concept was developed in this work. The methods validation was done by statistic analysis and comparison with the time series generated by the B¨ackar Discrete Wavelet Transfom (DWT) used by [1]. Besides of this, the idea that the DWPT is more because of its processing flexibility in the time-frequency plan was validated.

Este trabalho visa dar uma contribuição para o aumento de eficiência no fluxo de projeto de sistemas integrados, especificamente na avaliação de desempenho da comunicação entre os seus blocos componentes. É proposto o uso de modelagem e simulação de hardware em alto nível, no nível de transações, denominado TLM, para aproveitar a redução de esforço e tempo que se pode oferecer ao projeto de sistemas integrados, diferentemente de enfoques convencionais em níveis mais baixos de descrição, como o nível de registradores (RTL). É proposta uma forma de análise do tráfego intrachip produzido na comunicação de elementos do sistema, visando-se o uso dos resultados obtidos para descrição de geradores de tráfego. A principal contribuição deste trabalho é a proposta da análise de séries de tráfego obtido durante simulação de plataformas de hardware descritas no nível TLM usando-se métodos estatísticos conhecidos da área de estudo de séries temporais. A análise permite ao projetista ter maior compreensão da natureza estatística do tráfego intrachip, denominada dependência de curta ou longa duração (SRD e LRD), para o posterior ajuste de modelos usados na geração de séries sintéticas que representem tal natureza. Os resultados da análise mostraram que o tráfego obtido por simulação TLM tem natureza similar em relação ao da do tráfego obtido por simulação num nível mais baixo de abstração, do tipo de precisão por ciclos, indicando que o tráfego TLM pode ser usado para a representação do tráfego intrachip. Outra contribuição deste trabalho é a proposta de ajuste de modelos paramétricos autossimilares usando-se a decomposição da série de tráfego original, tendo sido feita uma comparação dos resultados desta com o ajuste convencional feito a modelos sem decomposição. Estas contribuições foram agrupadas dentro de uma metodologia detalhada, apresentada neste documento, para a qual experimentos foram realizados. Os resultados a partir das séries sintéticas autossimilares geradas pelos modelos estimados, apresentaram semelhança nos indicadores de SRD e LRD em relação às séries originais TLM, mostrando ser favorável o uso futuro destas séries sintéticas na implementação de geradores de tráfego. / It is objective of this work to make a contribution to improve the efficiency of the integrated systems design flow, specifically on the evaluation of communication performance between component blocks. The use of high level hardware modeling and simulation, at the transaction level, known as TLM, is proposed, in order to take advantage of the reduction of effort and time for the integrated system design; that in contrast to the traditional approaches, which use lower hardware description level, such as register transfer level (RTL). A methodology to evaluate the intra-chip traffic produced by the communication between system elements is proposed. The main contribution of this work is the analysis of traffic time series obtained by simulation of hardware platforms modeled in TLM, using well-known statistical methods for time series analysis. The analysis allows the system developer to understand the statistical nature of the intra-chip traffic, also known as short and long range dependence (SRD and LRD), for later adjustment and accurate representation of the traffic nature in synthetic series. The analysis results have shown that traffic traces obtained by TLM simulation has similar statistical nature as the traffic traces obtained at lower abstraction level, as cycle accurate type, which indicates that TLM traffic could be used to represent intrachip traffic. Another contribution of this work is a fitting procedure to auto similar parametric models thought the decomposition of the original traffic, and its comparison to the results of the conventional fitting, when applied to models that are not decomposed. These contributions were grouped and included in the detailed methodology presented in this document, being a series of experiments carried out. The results related to self-similar synthetic series, obtained from the fitted models, have shown similarity to the SRD and LRD indicators of the original TLM series, what favors the use of synthetic series future for the implementation of traffic generators.

Estudos mostraram que o tráfego nas redes de dados tanto locais quanto de grande área, possui propriedades fractais como dependência de longa duração - Long-Range Dependence (LRD) e auto-similaridade. Devido à heterogeneidade de aplicações nessas redes, os traces de tráfego podem apresentar dependência de longa duração - Long Range Dependence (LRD), dependência de curta duração - Short Range Dependence (SRD) ou uma mistura de LRD com SRD. Sendo assim, este trabalho tem como objetivo sintetizar séries temporais gaussianas com flexibilidade de processamento no plano tempo-frequência a serem inseridas num gerador de tráfego com as características estatísticas específicas do tráfego encontrado em redes por comutação de pacotes reais, como autossimilaridade, LRD e SRD. Para isto foram desenvolvidos dois métodos para síntese de séries temporais gaussianas com LRD e simultânea introdução de SRD em diferentes faixas de frequência: Discrete Wavelet Tansform (DWT) com mapa de variâncias e Discrete Wavelet Packet Tansform (DWPT). Estes métodos utilizaram o mapa de variâncias cujo conceito foi desenvolvido neste trabalho. A validação dos métodos foi feita através de análise estatística e comparação com resultados de séries geradas pelo método Discrete Wavelet Transfom (DWT) de Backar utilizado em [1]. Além disso, também foi validada a ideia de que a DWPT é mais interessante que a DWT por ser mais flexível e prover uma maior flexibilidade de processamento no plano tempo-frequência. / Studies demonstrated that the data network traffic of Local Area Network (LAN) and Wide Area Network has fractal properties as long range dependence (LRD) and self-similarity. The traffic traces can show long range dependence, short range dependence or the both behaviors because of applications heterogeneity in these networks. This work objective is to synthetisize gaussian time series with processor flexibility in the time-frequency plan to be inserted in a traffic generator with the specific statistical traffic characteristics of real packet networks such as selfsimilarity, long range dependence (LRD) and short range dependence (SRD). Two methods were developed for the gaussian time series with LRD and SRD synthesis: Discrete Wavelet Tansform (DWT) with variance map and Discrete Wavelet Packet Tansform (DWPT). These methods used the variance map which concept was developed in this work. The methods validation was done by statistic analysis and comparison with the time series generated by the B¨ackar Discrete Wavelet Transfom (DWT) used by [1]. Besides of this, the idea that the DWPT is more because of its processing flexibility in the time-frequency plan was validated.

It is desirable to reduce the potential threats that result from the variability of nature, such as droughts or heat waves that lead to food shortage, or the other extreme, floods that lead to severe damage. To prevent such catastrophic events, it is necessary to understand, and to be capable of characterising, nature's variability. Typically one aims to describe the underlying dynamics of geophysical records with differential equations. There are, however, situations where this does not support the objectives, or is not feasible, e.g., when little is known about the system, or it is too complex for the model parameters to be identified. In such situations it is beneficial to regard certain influences as random, and describe them with stochastic processes. In this thesis I focus on such a description with linear stochastic processes of the FARIMA type and concentrate on the detection of long-range dependence. Long-range dependent processes show an algebraic (i.e. slow) decay of the autocorrelation function. Detection of the latter is important with respect to, e.g. trend tests and uncertainty analysis. Aiming to provide a reliable and powerful strategy for the detection of long-range dependence, I suggest a way of addressing the problem which is somewhat different from standard approaches. Commonly used methods are based either on investigating the asymptotic behaviour (e.g., log-periodogram regression), or on finding a suitable potentially long-range dependent model (e.g., FARIMA[p,d,q]) and test the fractional difference parameter d for compatibility with zero. Here, I suggest to rephrase the problem as a model selection task, i.e.comparing the most suitable long-range dependent and the most suitable short-range dependent model. Approaching the task this way requires a) a suitable class of long-range and short-range dependent models along with suitable means for parameter estimation and b) a reliable model selection strategy, capable of discriminating also non-nested models. With the flexible FARIMA model class together with the Whittle estimator the first requirement is fulfilled. Standard model selection strategies, e.g., the likelihood-ratio test, is for a comparison of non-nested models frequently not powerful enough. Thus, I suggest to extend this strategy with a simulation based model selection approach suitable for such a direct comparison. The approach follows the procedure of a statistical test, with the likelihood-ratio as the test statistic. Its distribution is obtained via simulations using the two models under consideration. For two simple models and different parameter values, I investigate the reliability of p-value and power estimates obtained from the simulated distributions. The result turned out to be dependent on the model parameters. However, in many cases the estimates allow an adequate model selection to be established. An important feature of this approach is that it immediately reveals the ability or inability to discriminate between the two models under consideration. Two applications, a trend detection problem in temperature records and an uncertainty analysis for flood return level estimation, accentuate the importance of having reliable methods at hand for the detection of long-range dependence. In the case of trend detection, falsely concluding long-range dependence implies an underestimation of a trend and possibly leads to a delay of measures needed to take in order to counteract the trend. Ignoring long-range dependence, although present, leads to an underestimation of confidence intervals and thus to an unjustified belief in safety, as it is the case for the return level uncertainty analysis. A reliable detection of long-range dependence is thus highly relevant in practical applications. Examples related to extreme value analysis are not limited to hydrological applications. The increased uncertainty of return level estimates is a potentially problem for all records from autocorrelated processes, an interesting examples in this respect is the assessment of the maximum strength of wind gusts, which is important for designing wind turbines. The detection of long-range dependence is also a relevant problem in the exploration of financial market volatility. With rephrasing the detection problem as a model selection task and suggesting refined methods for model comparison, this thesis contributes to the discussion on and development of methods for the detection of long-range dependence. / Die potentiellen Gefahren und Auswirkungen der natürlicher Klimavariabilitäten zu reduzieren ist ein wünschenswertes Ziel. Solche Gefahren sind etwa Dürren und Hitzewellen, die zu Wasserknappheit führen oder, das andere Extrem, Überflutungen, die einen erheblichen Schaden an der Infrastruktur nach sich ziehen können. Um solche katastrophalen Ereignisse zu vermeiden, ist es notwendig die Dynamik der Natur zu verstehen und beschreiben zu können. Typischerweise wird versucht die Dynamik geophysikalischer Datenreihen mit Differentialgleichungssystemen zu beschreiben. Es gibt allerdings Situationen in denen dieses Vorgehen nicht zielführend oder technisch nicht möglich ist. Dieses sind Situationen in denen wenig Wissen über das System vorliegt oder es zu komplex ist um die Modellparameter zu identifizieren. Hier ist es sinnvoll einige Einflüsse als zufällig zu betrachten und mit Hilfe stochastischer Prozesse zu modellieren. In dieser Arbeit wird eine solche Beschreibung mit linearen stochastischen Prozessen der FARIMA-Klasse angestrebt. Besonderer Fokus liegt auf der Detektion von langreichweitigen Korrelationen. Langreichweitig korrelierte Prozesse sind solche mit einer algebraisch, d.h. langsam, abfallenden Autokorrelationsfunktion. Eine verläßliche Erkennung dieser Prozesse ist relevant für Trenddetektion und Unsicherheitsanalysen. Um eine verläßliche Strategie für die Detektion langreichweitig korrelierter Prozesse zur Verfügung zu stellen, wird in der Arbeit ein anderer als der Standardweg vorgeschlagen. Gewöhnlich werden Methoden eingesetzt, die das asymptotische Verhalten untersuchen, z.B. Regression im Periodogramm. Oder aber es wird versucht ein passendes potentiell langreichweitig korreliertes Modell zu finden, z.B. aus der FARIMA Klasse, und den geschätzten fraktionalen Differenzierungsparameter d auf Verträglichkeit mit dem trivialen Wert Null zu testen. In der Arbeit wird vorgeschlagen das Problem der Detektion langreichweitiger Korrelationen als Modellselektionsproblem umzuformulieren, d.h. das beste kurzreichweitig und das beste langreichweitig korrelierte Modell zu vergleichen. Diese Herangehensweise erfordert a) eine geeignete Klasse von lang- und kurzreichweitig korrelierten Prozessen und b) eine verläßliche Modellselektionsstrategie, auch für nichtgenestete Modelle. Mit der flexiblen FARIMA-Klasse und dem Whittleschen Ansatz zur Parameterschätzung ist die erste Voraussetzung erfüllt. Hingegen sind standard Ansätze zur Modellselektion, wie z.B. der Likelihood-Ratio-Test, für nichtgenestete Modelle oft nicht trennscharf genug. Es wird daher vorgeschlagen diese Strategie mit einem simulationsbasierten Ansatz zu ergänzen, der insbesondere für die direkte Diskriminierung nichtgenesteter Modelle geeignet ist. Der Ansatz folgt einem statistischen Test mit dem Quotienten der Likelihood als Teststatistik. Ihre Verteilung wird über Simulationen mit den beiden zu unterscheidenden Modellen ermittelt. Für zwei einfache Modelle und verschiedene Parameterwerte wird die Verläßlichkeit der Schätzungen für p-Wert und Power untersucht. Das Ergebnis hängt von den Modellparametern ab. Es konnte jedoch in vielen Fällen eine adäquate Modellselektion etabliert werden. Ein wichtige Eigenschaft dieser Strategie ist, dass unmittelbar offengelegt wird, wie gut sich die betrachteten Modelle unterscheiden lassen. Zwei Anwendungen, die Trenddetektion in Temperaturzeitreihen und die Unsicherheitsanalyse für Bemessungshochwasser, betonen den Bedarf an verläßlichen Methoden für die Detektion langreichweitiger Korrelationen. Im Falle der Trenddetektion führt ein fälschlicherweise gezogener Schluß auf langreichweitige Korrelationen zu einer Unterschätzung eines Trends, was wiederum zu einer möglicherweise verzögerten Einleitung von Maßnahmen führt, die diesem entgegenwirken sollen. Im Fall von Abflußzeitreihen führt die Nichtbeachtung von vorliegenden langreichweitigen Korrelationen zu einer Unterschätzung der Unsicherheit von Bemessungsgrößen. Eine verläßliche Detektion von langreichweitig Korrelierten Prozesse ist somit von hoher Bedeutung in der praktischen Zeitreihenanalyse. Beispiele mit Bezug zu extremem Ereignissen beschränken sich nicht nur auf die Hochwasseranalyse. Eine erhöhte Unsicherheit in der Bestimmung von extremen Ereignissen ist ein potentielles Problem von allen autokorrelierten Prozessen. Ein weiteres interessantes Beispiel ist hier die Abschätzung von maximalen Windstärken in Böen, welche bei der Konstruktion von Windrädern eine Rolle spielt. Mit der Umformulierung des Detektionsproblems als Modellselektionsfrage und mit der Bereitstellung geeigneter Modellselektionsstrategie trägt diese Arbeit zur Diskussion und Entwicklung von Methoden im Bereich der Detektion von langreichweitigen Korrelationen bei.

The field of the dissertation research is features of computer network packet traffic, the impact of network node features on traffic service, methods of real-time analysis of network traffic features and their application for dynamic prognostication of computer network packet traffic variance. The object of the research is the features of computer network packet traffic, the impact of network node features on computer network traffic service, methods of real-time network traffic features analysis and their application for dynamic prognostication of network traffic variances. The aim of work is to investigate fractal processes in computer networks, grounding on the results obtained to select methods suitable for real-time analysis of network traffic and to work out methods for real-time measurement of self-similarity as well as to apply it for perfection of computer networks service quality. Possibilities for mathematical modelling of network components, computer network packet traffic models and models using service theory instruments have been analysed. The package of network traffic features analysis has been worked out; it was used for analysis, assessment and comparison of methods for computer networks fractality and self-similarity research. For assessment of self-similarity of the network traffic time lines analysis, frequency/wave feature estimates, self-similarity analysis methods based on time line stability parameters estimators and assessed by the chaos theory... [to full text] / Disertacijos tyrimų sritis – kompiuterių tinklo paketinio srauto savybės, tinklo mazgo savybių įtaka srauto aptarnavimui, tinklo srauto savybių realaus laiku analizės metodai ir jų taikymas kompiuterių tinklo srauto kaitos dinaminiam prognozavimui. Tyrimų objektas – kompiuterių tinklo paketinio srauto savybės, tinklo mazgo savybių įtaka paketinio kompiuterių tinklo srauto aptarnavimui, realaus laiko tinklo srauto savybių analizės metodai ir jų taikymas tinklo srauto kaitos dinaminiam prognozavimui. Darbo tikslas – ištirti fraktalinius procesus kompiuterių tinkluose, remiantis gautais rezultatais parinkti metodus, tinkamus tinklo srauto analizei realiu laiku, ir sukurti savastingumo matavimo realiu laiku metodiką bei ją pritaikyti kompiuterių tinklų aptarnavimo kokybei gerinti. Išanalizuotos tinklo komponentų matematinio modeliavimo galimybės, kompiuterių tinklo paketinio srauto modeliai ir modeliai, naudojantys aptarnavimo teorijos instrumentus. Parengtas tinklo srauto savybių analizės paketas, panaudotas kompiuterių tinklų fraktališkumo ir savastingumo tyrimo metodams analizuoti, vertinti ir palyginti. Ištirti paketinio kompiuterių tinklo srauto laiko eilučių analizės, dažninių/banginių savybių įvertinimo, laiko eilutės stabilumo parametrų įverčiais grindžiami bei chaoso teorijos priemonėmis įvertinami savastingumo analizės metodai. Sudarytas tinklo srauto savastingumo realiu laiku analizės paketas, kurį naudojant savastingumo matavimui realiu laiku atrinktas robastinis... [toliau žr. visą tekstą]

The aim of this thesis is to estimate the volatility function of continuoustime stochastic models. The estimation of the volatility of the following wellknown international stock market indexes is presented as an application: Dow Jones Industrial Average, Standard and Poor’s 500, NIKKEI 225, CAC 40, DAX 30, FTSE 100 and IBEX 35. This estimation is studied from two different perspectives: a) assuming that the volatility of the stock market indexes displays shortrange dependence (SRD), and b) extending the previous model for processes with longrange dependence (LRD), intermediaterange dependence (IRD) or SRD. Under the efficient market hypothesis (EMH), the compatibility of the Vasicek, the CIR, the Anh and Gao, and the CKLS models with the stock market indexes is being tested. Nonparametric techniques are presented to test the affinity of these parametric volatility functions with the volatility observed from the data. Under the assumption of possible statistical patterns in the volatility process, a new estimation procedure based on the Whittle estimation is proposed. This procedure is theoretically and empirically proven. In addition, its application to the stock market indexes provides interesting results.

Este trabalho visa dar uma contribuição para o aumento de eficiência no fluxo de projeto de sistemas integrados, especificamente na avaliação de desempenho da comunicação entre os seus blocos componentes. É proposto o uso de modelagem e simulação de hardware em alto nível, no nível de transações, denominado TLM, para aproveitar a redução de esforço e tempo que se pode oferecer ao projeto de sistemas integrados, diferentemente de enfoques convencionais em níveis mais baixos de descrição, como o nível de registradores (RTL). É proposta uma forma de análise do tráfego intrachip produzido na comunicação de elementos do sistema, visando-se o uso dos resultados obtidos para descrição de geradores de tráfego. A principal contribuição deste trabalho é a proposta da análise de séries de tráfego obtido durante simulação de plataformas de hardware descritas no nível TLM usando-se métodos estatísticos conhecidos da área de estudo de séries temporais. A análise permite ao projetista ter maior compreensão da natureza estatística do tráfego intrachip, denominada dependência de curta ou longa duração (SRD e LRD), para o posterior ajuste de modelos usados na geração de séries sintéticas que representem tal natureza. Os resultados da análise mostraram que o tráfego obtido por simulação TLM tem natureza similar em relação ao da do tráfego obtido por simulação num nível mais baixo de abstração, do tipo de precisão por ciclos, indicando que o tráfego TLM pode ser usado para a representação do tráfego intrachip. Outra contribuição deste trabalho é a proposta de ajuste de modelos paramétricos autossimilares usando-se a decomposição da série de tráfego original, tendo sido feita uma comparação dos resultados desta com o ajuste convencional feito a modelos sem decomposição. Estas contribuições foram agrupadas dentro de uma metodologia detalhada, apresentada neste documento, para a qual experimentos foram realizados. Os resultados a partir das séries sintéticas autossimilares geradas pelos modelos estimados, apresentaram semelhança nos indicadores de SRD e LRD em relação às séries originais TLM, mostrando ser favorável o uso futuro destas séries sintéticas na implementação de geradores de tráfego. / It is objective of this work to make a contribution to improve the efficiency of the integrated systems design flow, specifically on the evaluation of communication performance between component blocks. The use of high level hardware modeling and simulation, at the transaction level, known as TLM, is proposed, in order to take advantage of the reduction of effort and time for the integrated system design; that in contrast to the traditional approaches, which use lower hardware description level, such as register transfer level (RTL). A methodology to evaluate the intra-chip traffic produced by the communication between system elements is proposed. The main contribution of this work is the analysis of traffic time series obtained by simulation of hardware platforms modeled in TLM, using well-known statistical methods for time series analysis. The analysis allows the system developer to understand the statistical nature of the intra-chip traffic, also known as short and long range dependence (SRD and LRD), for later adjustment and accurate representation of the traffic nature in synthetic series. The analysis results have shown that traffic traces obtained by TLM simulation has similar statistical nature as the traffic traces obtained at lower abstraction level, as cycle accurate type, which indicates that TLM traffic could be used to represent intrachip traffic. Another contribution of this work is a fitting procedure to auto similar parametric models thought the decomposition of the original traffic, and its comparison to the results of the conventional fitting, when applied to models that are not decomposed. These contributions were grouped and included in the detailed methodology presented in this document, being a series of experiments carried out. The results related to self-similar synthetic series, obtained from the fitted models, have shown similarity to the SRD and LRD indicators of the original TLM series, what favors the use of synthetic series future for the implementation of traffic generators.

<p>This thesis, consisting of three papers and a summary, studies topics in the theory of stochastic processes related to long-range dependence. Much recent interest in such probabilistic models has its origin in measurements of Internet traffic data, where typical characteristics of long memory have been observed. As a macroscopic feature, long-range dependence can be mathematically studied using certain scaling limit theorems. </p><p>Using such limit results, two different scaling regimes for Internet traffic models have been identified earlier. In one of these regimes traffic at large scales can be approximated by long-range dependent Gaussian or stable processes, while in the other regime the rescaled traffic fluctuates according to stable ``memoryless'' processes with independent increments. In Paper I a similar limit result is proved for a third scaling scheme, emerging as an intermediate case of the other two. The limit process here turns out to be a non-Gaussian and non-stable process with long-range dependence.</p><p>In Paper II we derive a representation for the latter limit process as a stochastic integral of a deterministic function with respect to a certain compensated Poisson random measure. This representation enables us to study some further properties of the process. In particular, we prove that the process at small scales behaves like a Gaussian process with long-range dependence, while at large scales it is close to a stable process with independent increments. Hence, the process can be regarded as a link between these two processes of completely different nature.</p><p>In Paper III we construct a class of processes locally behaving as Gaussian and globally as stable processes and including the limit process obtained in Paper I. These processes can be chosen to be long-range dependent and are potentially suitable as models in applications with distinct local and global behaviour. They are defined using stochastic integrals with respect to the same compensated Poisson random measure as used in Paper II.</p>

This thesis, consisting of three papers and a summary, studies topics in the theory of stochastic processes related to long-range dependence. Much recent interest in such probabilistic models has its origin in measurements of Internet traffic data, where typical characteristics of long memory have been observed. As a macroscopic feature, long-range dependence can be mathematically studied using certain scaling limit theorems. Using such limit results, two different scaling regimes for Internet traffic models have been identified earlier. In one of these regimes traffic at large scales can be approximated by long-range dependent Gaussian or stable processes, while in the other regime the rescaled traffic fluctuates according to stable ``memoryless'' processes with independent increments. In Paper I a similar limit result is proved for a third scaling scheme, emerging as an intermediate case of the other two. The limit process here turns out to be a non-Gaussian and non-stable process with long-range dependence. In Paper II we derive a representation for the latter limit process as a stochastic integral of a deterministic function with respect to a certain compensated Poisson random measure. This representation enables us to study some further properties of the process. In particular, we prove that the process at small scales behaves like a Gaussian process with long-range dependence, while at large scales it is close to a stable process with independent increments. Hence, the process can be regarded as a link between these two processes of completely different nature. In Paper III we construct a class of processes locally behaving as Gaussian and globally as stable processes and including the limit process obtained in Paper I. These processes can be chosen to be long-range dependent and are potentially suitable as models in applications with distinct local and global behaviour. They are defined using stochastic integrals with respect to the same compensated Poisson random measure as used in Paper II.