Parameter identification in time series models

Time series analysis can be categorized into three different approaches: classical, Box-Jenkins, and State space. Classical approach makes a basement for the analysis and Box-Jenkins approach is an improvement of the classical approach and deals with stationary time series. State space approach allows time variant factors and covers up a broader area of time series analysis. This thesis focuses on parameter identifiablity of different parameter estimation methods such as LSQ, Yule-Walker, MLE which are used in the above time series analysis approaches. Also the Kalman filter method and smoothing techniques are integrated with the state space approach and MLE method to estimate parameters allowing them to change over time. Parameter estimation is carried out by repeating estimation and integrating with MCMC and inspect how well different estimation methods can identify the optimal model parameters. Identification is performed in probabilistic and general senses and compare the results in order to study and represent identifiability more informative way.

Initialization of Continuous Nonlinear Models Using Extended Kalman Filter

The two main objectives of Bayesian inference are to estimate parameters and states. In this thesis, we are interested in how this can be done in the framework of state-space models when there is a complete or partial lack of knowledge of the initial state of a continuous nonlinear dynamical system. In literature, similar problems have been referred to as diffuse initialization problems. This is achieved first by extending the previously developed diffuse initialization Kalman filtering techniques for discrete systems to continuous systems. The second objective is to estimate parameters using MCMC methods with a likelihood function obtained from the diffuse filtering. These methods are tried on the data collected from the 1995 Ebola outbreak in Kikwit, DRC in order to estimate the parameters of the system.

Ionsonde Measurement Trend

Time series analysis has gone through different developmental stages before the current modern approaches. These can broadly categorized as the classical time series analysis and modern time series analysis approach. In the classical one, the basic target of the analysis is to describe the major behaviour of the series without necessarily dealing with the underlying structures. On the contrary, the modern approaches strives to summarize the behaviour of the series going through its underlying structure so that the series can be represented explicitly. In other words, such approach of time series analysis tries to study the series structurally. The components of the series that make up the observation such as the trend, seasonality, regression and disturbance terms are modelled explicitly before putting everything together in to a single state space model which give the natural interpretation of the series. The target of this diploma work is to practically apply the modern approach of time series analysis known as the state space approach, more specifically, the dynamic linear model, to make trend analysis over Ionosonde measurement data. The data is time series of the peak height of F2 layer symbolized by hmF2 which is the height of high electron density. In addition, the work also targets to investigate the connection between solar activity and the peak height of F2 layer. Based on the result found, the peak height of the F2 layer has shown a decrease during the observation period and also shows a nonlinear positive correlation with solar activity.

Latent Variable Models for Stochastic Discount Factors.

Latent variable models in finance originate both from asset pricing theory and time series analysis. These two strands of literature appeal to two different concepts of latent structures, which are both useful to reduce the dimension of a statistical model specified for a multivariate time series of asset prices. In the CAPM or APT beta pricing models, the dimension reduction is cross-sectional in nature, while in time-series state-space models, dimension is reduced longitudinally by assuming conditional independence between consecutive returns, given a small number of state variables. In this paper, we use the concept of Stochastic Discount Factor (SDF) or pricing kernel as a unifying principle to integrate these two concepts of latent variables. Beta pricing relations amount to characterize the factors as a basis of a vectorial space for the SDF. The coefficients of the SDF with respect to the factors are specified as deterministic functions of some state variables which summarize their dynamics. In beta pricing models, it is often said that only the factorial risk is compensated since the remaining idiosyncratic risk is diversifiable. Implicitly, this argument can be interpreted as a conditional cross-sectional factor structure, that is, a conditional independence between contemporaneous returns of a large number of assets, given a small number of factors, like in standard Factor Analysis. We provide this unifying analysis in the context of conditional equilibrium beta pricing as well as asset pricing with stochastic volatility, stochastic interest rates and other state variables. We address the general issue of econometric specifications of dynamic asset pricing models, which cover the modern literature on conditionally heteroskedastic factor models as well as equilibrium-based asset pricing models with an intertemporal specification of preferences and market fundamentals. We interpret various instantaneous causality relationships between state variables and market fundamentals as leverage effects and discuss their central role relative to the validity of standard CAPM-like stock pricing and preference-free option pricing.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

An Error-Localization, Validation and Optimization Tool for Embedded Code Augmentation: an Architecture Oriented Approach

Embedded systems are usually designed for a single or a specified set of tasks. This specificity means the system design as well as its hardware/software development can be highly optimized. Embedded software must meet the requirements such as high reliability operation on resource-constrained platforms, real time constraints and rapid development. This necessitates the adoption of static machine codes analysis tools running on a host machine for the validation and optimization of embedded system codes, which can help meet all of these goals. This could significantly augment the software quality and is still a challenging field.Embedded systems are usually designed for a single or a specified set of tasks. This specificity means the system design as well as its hardware/software development can be highly optimized. Embedded software must meet the requirements such as high reliability operation on resource-constrained platforms, real time constraints and rapid development. This necessitates the adoption of static machine codes analysis tools running on a host machine for the validation and optimization of embedded system codes, which can help meet all of these goals. This could significantly augment the software quality and is still a challenging field.Embedded systems are usually designed for a single or a specified set of tasks. This specificity means the system design as well as its hardware/software development can be highly optimized. Embedded software must meet the requirements such as high reliability operation on resource-constrained platforms, real time constraints and rapid development. This necessitates the adoption of static machine codes analysis tools running on a host machine for the validation and optimization of embedded system codes, which can help meet all of these goals. This could significantly augment the software quality and is still a challenging field.Embedded systems are usually designed for a single or a specified set of tasks. This specificity means the system design as well as its hardware/software development can be highly optimized. Embedded software must meet the requirements such as high reliability operation on resource-constrained platforms, real time constraints and rapid development. This necessitates the adoption of static machine codes analysis tools running on a host machine for the validation and optimization of embedded system codes, which can help meet all of these goals. This could significantly augment the software quality and is still a challenging field.This dissertation contributes to an architecture oriented code validation, error localization and optimization technique assisting the embedded system designer in software debugging, to make it more effective at early detection of software bugs that are otherwise hard to detect, using the static analysis of machine codes. The focus of this work is to develop methods that automatically localize faults as well as optimize the code and thus improve the debugging process as well as quality of the code.Validation is done with the help of rules of inferences formulated for the target processor. The rules govern the occurrence of illegitimate/out of place instructions and code sequences for executing the computational and integrated peripheral functions. The stipulated rules are encoded in propositional logic formulae and their compliance is tested individually in all possible execution paths of the application programs. An incorrect sequence of machine code pattern is identified using slicing techniques on the control flow graph generated from the machine code.An algorithm to assist the compiler to eliminate the redundant bank switching codes and decide on optimum data allocation to banked memory resulting in minimum number of bank switching codes in embedded system software is proposed. A relation matrix and a state transition diagram formed for the active memory bank state transition corresponding to each bank selection instruction is used for the detection of redundant codes. Instances of code redundancy based on the stipulated rules for the target processor are identified.This validation and optimization tool can be integrated to the system development environment. It is a novel approach independent of compiler/assembler, applicable to a wide range of processors once appropriate rules are formulated. Program states are identified mainly with machine code pattern, which drastically reduces the state space creation contributing to an improved state-of-the-art model checking. Though the technique described is general, the implementation is architecture oriented, and hence the feasibility study is conducted on PIC16F87X microcontrollers. The proposed tool will be very useful in steering novices towards correct use of difficult microcontroller features in developing embedded systems.

Queues with inter- ruption in Random/ Markovian Environment

In many situations probability models are more realistic than deterministic models. Several phenomena occurring in physics are studied as random phenomena changing with time and space. Stochastic processes originated from the needs of physicists.Let X(t) be a random variable where t is a parameter assuming values from the set T. Then the collection of random variables {X(t), t ∈ T} is called a stochastic process. We denote the state of the process at time t by X(t) and the collection of all possible values X(t) can assume, is called state space

Institutionalism Theories and Hegemonic Practices in Global Polity Formation

Institutionalistische Theorien und hegemoniale Praktiken Globaler Politikgestaltung. Eine neue Beleuchtung der Prämissen Liberaler Demokratischer National-Staatlicher Ordnungen. Deutsche Zusammenfassung: Moderne Sozialwissenschaften, seien es Metatheorien der Internationalen Beziehungen, die Geschichte politischer Ökonomie oder Institutionentheorien, zeigen eine klare Dreiteilung von Weltanschauungen bzw. Paradigmen auf, die sich in allen „großen Debatten“ nachvollziehen lassen: Realismus, Liberalismus und Historischer Materialismus. Diese Grund legend unterschiedlichen Paradigmen lassen sich auch in aktuellen Ansätzen des Institutionalismus aufzeigen, liegen aber quer zu den von anderen Wissenschaftlern (Meyer, Rittberger, Hasenclever, Peters, Zangl) vorgenommenen Kategorisierungen der Institutionalismusschulen, die systemkritische Perspektiven in der Regel ignorieren oder vergleichsweise rudimentär diskutieren. Deshalb entwickelt diese Arbeit einen Vergleich von Institutionalismusschulen entlang der oben skizzierten Weltanschauungen. Das Ziel ist es, fundamentale Unterschiede zwischen den drei Paradigmen zu verdeutlichen und zu zeigen, wie ihre jeweiligen ontologischen und epistemologischen Prämissen die Forschungsdesigns und Methodologien der Institutionalismusschulen beeinflussen. In Teil I arbeite ich deshalb die Grund legenden Prämissen der jeweiligen Paradigmen heraus und entwickle in Teil II und III diesen Prämissen entsprechende Institutionalismus-Schulen, die Kooperation primär als Organisation von unüberwindbarer Rivalität, als Ergebnis zunehmender Konvergenz, oder als Ergebnis und Weiterentwicklung von Prozeduren der Interaktion versteht. Hier greife ich auf zeitgenössische Arbeiten anderer Autoren zurück und liefere damit einen Vergleich des aktuellen Forschungsstandes in allen drei Denktraditionen. Teil II diskutiert die zwei dominanten Institutionalismusschulen und Teil III entwickelt einen eigenen Gramscianischen Ansatz zur Erklärung von internationaler Kooperation und Institutionalisierung. Die übergeordnete These dieser Arbeit lautet, dass die Methodologien der dominanten Institutionalismusschulen teleologische Effekte haben, die aus dem Anspruch auf universell anwendbare, abstrahiert Konzepte resultieren und die Interpretation von Beobachtungen limitieren. Prämissen eines rational handelnden Individuums - entweder Konsequenzen kalkulierend oder Angemessenheit reflektierend – führen dazu, dass Kooperation und Institutionalisierung notwendiger Weise als die beste Lösung für alle Beteiligten in dieser Situation gelten müssen: Institutionen würden nicht bestehen, wenn sie nicht in der Summe allen Mitgliedern (egoistisch oder kooperativ motiviert) nützten. Durch diese interpretative „Brille“ finden wichtige strukturelle Gründe für die Verabschiedung internationaler Abkommen und Teile ihrer Effekte keine Berücksichtigung. Folglich können auch Abweichungen von erwarteten Ergebnissen nicht hinreichend erklärt werden. Meine entsprechende Hypothese lautet, dass systemkritische Kooperation konsistenter erklären können, da sie Akteure, Strukturen und die sie umgebenden Weltanschauungen selbst als analytische Kriterien berücksichtigen. Institutionalisierung wird dann als ein gradueller Prozess politischer Entscheidungsfindung, –umsetzung und –verankerung verstanden, der durch die vorherrschenden Institutionen und Interpretationen von „Realität“ beeinflusst wird. Jede politische Organisation wird als zeitlich-geographisch markierter Staatsraum (state space) verstanden, dessen Mandat die Festlegung von Prozeduren der Interaktion für gesellschaftliche Entwicklung ist. Politische Akteure handeln in Referenz auf diese offiziellen Prozeduren und reproduzieren und/oder verändern sie damit kontinuierlich. Institutionen werden damit als integraler Bestandteil gesellschaftlicher Entwicklungsprozesse verstanden und die Wirkungsmacht von Weltanschauungen – inklusive theoretischer Konzepte - berücksichtigt. Letztere leiten die Wahrnehmung und Interpretation von festgeschriebenen Regeln an und beeinflussen damit ihre empfundene Legitimation und Akzeptanz. Dieser Effekt wurde als „Staatsgeist“ („State Spirit“) von Montesquieu und Hegel diskutiert und von Antonio Gramsci in seiner Hegemonialtheorie aufgegriffen. Seine Berücksichtigung erlaubt eine konsistente Erklärung scheinbar irrationalen oder unangemessenen individuellen Entscheidens, sowie negativer Effekte konsensualer Abkommen. Zur Veranschaulichung der neu entwickelten Konzepte werden in Teil II existierende Fallstudien zur Welthandelsorganisation analysiert und herausgearbeitet, wie Weltanschauungen oder Paradigmen zu unterschiedlichen Erklärungen der Praxis führen. Während Teil II besonderes Augenmerk auf die nicht erklärten und innerhalb der dominanten Paradigmen nicht erklärbaren Beobachtungen legt, wendet Teil III die Gramscianischen Konzepte auf eben diese blinden Stellen an und liefert neue Einsichten. Im Ausblick wird problematisiert, dass scheinbar „neutrale“ wissenschaftliche Studien politische Positionen und Forderungen legitimieren und verdeutlicht im Sinne der gramscianischen Theorie, dass Wissenschaft selbst Teil politischer Auseinandersetzungen ist.

Taming Chaotic Circuits

Control algorithms that exploit chaotic behavior can vastly improve the performance of many practical and useful systems. The program Perfect Moment is built around a collection of such techniques. It autonomously explores a dynamical system's behavior, using rules embodying theorems and definitions from nonlinear dynamics to zero in on interesting and useful parameter ranges and state-space regions. It then constructs a reference trajectory based on that information and causes the system to follow it. This program and its results are illustrated with several examples, among them the phase-locked loop, where sections of chaotic attractors are used to increase the capture range of the circuit.

Triangulation by Continuous Embedding

When triangulating a belief network we aim to obtain a junction tree of minimum state space. Searching for the optimal triangulation can be cast as a search over all the permutations of the network's vaeriables. Our approach is to embed the discrete set of permutations in a convex continuous domain D. By suitably extending the cost function over D and solving the continous nonlinear optimization task we hope to obtain a good triangulation with respect to the aformentioned cost. In this paper we introduce an upper bound to the total junction tree weight as the cost function. The appropriatedness of this choice is discussed and explored by simulations. Then we present two ways of embedding the new objective function into continuous domains and show that they perform well compared to the best known heuristic.

Tensiones socio-espaciales por las ventas ambulantes en el espacio público en Bogotá a partir del decreto 456 de 2013. Estudio de caso: Plazoleta del Rosario

El aprovechamiento económico del espacio público constituye un fenómeno que pone a prueba la definición de lo público y lo privado. Esta distinción es una de las bases de la institucionalidad del Estado moderno, por lo que desafiarla genera tensiones que repercuten en su administración. Por su parte, los actores involucrados en la discusión de la racionalidad sobre la que se fundamentan los cimientos de nuestra democracia liberal, son agentes marginalizados a través de las diferentes clasificaciones que se aplican a ellos estigmatizándolos socialmente. Es a partir de esta dicotomía entre lo formal y lo informal y su manera de relacionarse, que se entra a discutir la construcción social del espacio público y las ambivalencias de los derechos de una población que actúa al margen del sistema.

Heuristic techniques for the analysis of variability as a dynamic aspect of change. 'Técnicas heurísticas para analizar la variabilidad como un aspecto dinámico de cambio'.

Monográfico con el título: 'Los mecanismos del cambio cognitivo'. Resumen basado en el de la publicación

ARX, model thinking: taxonomy and operativity of an architectural process

The purpose of this work was to establish a taxonomy of hand made model construction as a platform for an approach to project an operative method in architecture. It was therefore studied and catalogued in a systematic approach a broad model production in the work of ARX. A wide range of families and sub-families of models were found, with different purposes according to each phase of development, from searching steps for a new possible configuration to detailed refined decisions. This working method revealed as most relevant characteristics, the grounds for a potential personal reflection and open discussion on project method, its flexibility on space modeling, an accuracy on the representation of real construction situations and its constant and stimulating opening to new suggestions. This research helped on a meta-reflection about this method, having been useful on creating a consciousness of processes that pretend to become an autonomous language, knowledge that might become useful to those who pretend to implement a haptic modus operandi in the work of an architectural project.

Preferred structures in large-scale circulation and the effect of doubling greenhouse gas concentration in HadCM3

Preferred structures in the surface pressure variability are investigated in and compared between two 100-year simulations of the Hadley Centre climate model HadCM3. In the first (control) simulation, the model is forced with pre-industrial carbon dioxide concentration (1×CO2) and in the second simulation the model is forced with doubled CO2 concentration (2×CO2). Daily winter (December-January-February) surface pressures over the Northern Hemisphere are analysed. The identification of preferred patterns is addressed using multivariate mixture models. For the control simulation, two significant flow regimes are obtained at 5% and 2.5% significance levels within the state space spanned by the leading two principal components. They show a high pressure centre over the North Pacific/Aleutian Islands associated with a low pressure centre over the North Atlantic, and its reverse. For the 2×CO2 simulation, no such behaviour is obtained. At higher-dimensional state space, flow patterns are obtained from both simulations. They are found to be significant at the 1% level for the control simulation and at the 2.5% level for the 2×CO2 simulation. Hence under CO2 doubling, regime behaviour in the large-scale wave dynamics weakens. Doubling greenhouse gas concentration affects both the frequency of occurrence of regimes and also the pattern structures. The less frequent regime becomes amplified and the more frequent regime weakens. The largest change is observed over the Pacific where a significant deepening of the Aleutian low is obtained under CO2 doubling.

Variability of the North Atlantic eddy-driven jet stream

Much of the atmospheric variability in the North Atlantic sector is associated with variations in the eddy-driven component of the zonal flow. Here we present a simple method to specifically diagnose this component of the flow using the low-level wind field (925–700 hpa ). We focus on the North Atlantic winter season in the ERA-40 reanalysis. Diagnostics of the latitude and speed of the eddy-driven jet stream are compared with conventional diagnostics of the North Atlantic Oscillation (NAO) and the East Atlantic (EA) pattern. This shows that the NAO and the EA both describe combined changes in the latitude and speed of the jet stream. It is therefore necessary, but not always sufficient, to consider both the NAO and the EA in identifying changes in the jet stream. The jet stream analysis suggests that there are three preferred latitudinal positions of the North Atlantic eddy-driven jet stream in winter. This result is in very good agreement with the application of a statistical mixture model to the two-dimensional state space defined by the NAO and the EA. These results are consistent with several other studies which identify four European/Atlantic regimes, comprising three jet stream patterns plus European blocking events.