Many financial time series show heavy tail behavior. Such tail characteristic is important for risk management. In this research, we focus on the calculation of Value-at-Risk (VaR) for portfolios of financial assets. We consider nonlinear autoregressive models with heavy tail innovations to model the return. Predictive distribution of the return are used to compute the VaR of the portfolios of financial assets. Examples are also given to compare the VaR computed by our approach with those by other methods.

Extracting the Electric Network Frequency (ENF) fluctuations from an audio recording and comparing it to a reference database is a new technology intended to perform forensic digital audio authentication. The objective of this thesis is to implement and design a range of programs and algorithms for capturing and extracting ENF signals. The developed C-program combined with a probe can be used to build the reference database. Our implementation of the Short-Time Fourier Transform method is intended for the ENF extraction of longer signals while our novel proposed use of the Autoregressive parametric method and our implementation of the zero-crossing approach tackle the case of shorter recordings. A Graphical User Interface (GUI) was developed to facilitate the process of extracting the ENF fluctuations. The whole process is tested and evaluated for various scenarios ranging from long to short recordings.

Extracting the Electric Network Frequency (ENF) fluctuations from an audio recording and comparing it to a reference database is a new technology intended to perform forensic digital audio authentication. The objective of this thesis is to implement and design a range of programs and algorithms for capturing and extracting ENF signals. The developed C-program combined with a probe can be used to build the reference database. Our implementation of the Short-Time Fourier Transform method is intended for the ENF extraction of longer signals while our novel proposed use of the Autoregressive parametric method and our implementation of the zero-crossing approach tackle the case of shorter recordings. A Graphical User Interface (GUI) was developed to facilitate the process of extracting the ENF fluctuations. The whole process is tested and evaluated for various scenarios ranging from long to short recordings.

En damm används i huvudsak för att magasinera vatten i energiutvinningssyfte. Dammen rör sig fram och tillbaka i ett säsongsmönster mestadels beroende på skillnader i utomhustemperatur och vattentemperaturen i magasinet. Det nordiska klimatet innebär risk för isläggning i magasinet, för vilken lasten är relativt outforskad. Denna rapport syftar till ett med multipla linjära regressionsmodeller samt dynamiska regressionsmodeller avgöra vilka variabler som förklarar en specifik svensk dammkonstruktions rörelse. Dammens rörelse mäts genom att mäta dammens förflyttning kontra berggrunden med data från dammens inverterade pendlar. Av särskilt intresse är att avgöra islastens påverkan på rörelsen. Resultaten visar att multipla linjära regressions-modeller inte fullständigt lyckas modellera dammens rörelse, då de har problem med autokorrelerade residualer. Detta hanteras med hjälp av autoregressiva regressionsmodeller där de initiala förklarande variablerna inkluderas, kallat dynamisk regression. Denna rapports resultat visar att de autoregressiva parametrarna fungerar mycket väl för att förklara pendlarna, men att även tid, temperatur, det hydrostatiska trycket samt istjocklek är användbara förklarande variabler. Istjockleken visar signifikant påverkan på 5 % signifikansnivå på två av de undersökta pendlarna, vilket är ett noterbart resultat. Författarna menar att rapportens resultat indikerar att det finns anledning att fortsätta forska kring islastens påverkan på dammkonstruktioner. / A dam is a structure mainly used for storing water and generating electricity. The structure of a dam moves in a season-based pattern, mainly because of the difference in temperature between the air on outside of the dam and the water on the inside. Due to the Nordic climate, occurrences of icing on the water in the basin is fairly frequent. The effects of ice on the structural load of the dam are relatively unexplored and are the subject to this bachelor’s thesis. The goal of this project is to evaluate which predictors are significant to the movement of the dam with multiple linear regression models and dynamic regressions. The movement is measured by inverted pendulums that register the dam’s movement compared to the foundation. It is of particular interest to determine if the ice load influences the movement of the dam. The multiple regression models used to explain the dam’s movement were all discarded due to autocorrelation in the residuals. This falsifies the models, since autocorrelation means that they don’t meet the needed assumptions. To counteract the autocorrelation, dynamic models with autoregressive terms were fitted. These models showed no problem with autocorrelation. The result from the dynamic models were successful and managed to significantly explain the movement of the dam. The autoregressive terms proved to be efficient explanatory variables. The dynamic regression models also show that the time, temperature, hydrostatic pressure and ice thickness variables are also useful explanatory variables. The ice thickness shows a significant effect at the 5 % significance level on two of the investigated pendulums. The report's results indicate that there is reason to continue research on the ice load impact on dam constructions.

VINTERA, J. Spectral anlysis with superesolution. Brno: University of Technology, The Faculty of Electrical Engineering and Communication, 2008. 85 p. Master’s thesis. This thesis deals with the topic of super-resolution spectral analysis in the Signal Processing Toolset. The Signal Processing Toolset is a software component of the LabVIEW 8.1. program equipment. The thesis consists of three main parts. In the first part the basic theoretic concepts of the Model-Based Frequency Analysis are described. The second part serves as a user manual for the super-resolution spectral analysis in the Signal Processing Toolset. The last part describes the application of the theory introduced in the first part, by means of testing the properties of the methods used by the Toolset.

This work is dedicated to study of the recognition and classification of cardiac disease, diagnosised through the electrocardiogram ECG. This examination is normally used in heart medical center, emergency, intensive therapy, and with complement diagnosis in heart disease as: acute myocardium infarction, bundle block branches, hypertrophy and others. The software was developed for support to the model, with focus on extraction of ECG signal characteristics, and an artificial neural network for recognition of diseases. For extraction these characteristics, we have used a auto-regressive model, AR, with the algorithm least mean square LMS, to minimize the minimum error. The neural network, with architecture multilayer perceptron and back propagation algorithm of training, was chosen for the recognition of the standards. The method was showed efficient. / Este trabalho dedica-se ao estudo do reconhecimento e classificação de cardiopatias, diagnosticadas através do exame de eletrocardiografia, ECG. Esse exame é comumente utilizado em visitas a cardiologistas, centros de emergência, centros de terapia intensiva e exames eletivos para auxílio de diagnóstico de cardiopatias como: infarto agudo do miocárdio, bloqueios de ramos, hipertrofia e outros. O aplicativo desenvolvido para apoio ao trabalho focaliza a extração de características do sinal ECG, representado por ciclos e a aplicação destas características a uma rede neural artificial para reconhecimento das cardiopatias. Para extração das características do sinal, utilizamos o modelo matemático de previsão de comportamento de curvas, chamado de auto-regressivo, AR, onde utilizamos o passado histórico recente da curva para determinar o próximo ponto; em nosso caso, utilizamos o algoritmo dos mínimos quadrados para adequação do erro, conhecido como LMS. A rede neural de topologia perceptron multicamadas e com algoritmo de treinamento backpropagation foi escolhida para o reconhecimento dos padrões, pela sua capacidade de generalização. O método se mostrou adequado e eficiente ao objetivo proposto. / Mestre em Ciências

This thesis deals with oportunity of ROC curve usage in the description of methods that work with sound signals. Specifically, it focuses on ways of detecting of stress in speech signals. The detection itselfs is done in a range of frequencies of the sound signal. There is also a classifier designed using ROC curves that decides whether the input signal is stressed or not. The output of this thesis are findings gathered from analyses and also some recommendation based on those analyses.