Im Rahmen des TERA-Projektes (Turbo Evaluation and Rapid Algorithms) wurde ein neuartiger, hochgradig effizienter probabilistischer Algorithmus zum Lösen polynomialer Gleichungssysteme entwickelt und für den komplexen Fall implementiert. Die Geometrie polarer Varietäten gestattet es, diesen Algorithmus zu einem Verfahren zur Charakterisierung der reellen Lösungsmengen polynomialer Gleichungssysteme zu erweitern. Ziel dieser Arbeit ist es, eine Implementierung dieses Verfahrens zur Bestimmung reeller Lösungen auf eine Klasse von Beispielproblemen anzuwenden. Dabei wurde Wert darauf gelegt, dass diese Beispiele reale, praxisbezogene Anwendungen besitzen. Diese Anforderung ist z.B. für polynomiale Gleichungssysteme erfüllt, die sich aus dem Entwurf von schnellen Wavelet-Transformationen ergeben. Die hier betrachteten Wavelet-Transformationen sollen die praktisch wichtigen Eigenschaften der Orthogonalität und Symmetrie besitzen. Die Konstruktion einer solchen Wavelet-Transformation hängt von endlich vielen reellen Parametern ab. Diese Parameter müssen gewisse polynomiale Gleichungen erfüllen. In der veröffentlichten Literatur zu diesem Thema wurden bisher ausschließlich Beispiele mit endlichen Lösungsmengen behandelt. Zur Berechnung dieser Beispiele war es dabei ausreichend, quadratische Gleichungen in einer oder zwei Variablen zu lösen. Zur Charakterisierung der reellen Lösungsmenge eines polynomialen Gleichungssystems ist es ein erster Schritt, in jeder reellen Zusammenhangskomponente mindestens einen Punkt aufzufinden. Schon dies ist ein intrinsisch schweres Problem. Es stellt sich heraus, dass der Algorithmus des TERA-Projektes zur Lösung dieser Aufgabe bestens geeignet ist und daher eine größere Anzahl von Beispielproblemen lösen kann als die besten kommerziell erhältlichen Lösungsverfahren. / As a result of the TERA-project on Turbo Evaluation and Rapid Algorithms a new type, highly efficient probabilistic algorithm for the solution of systems of polynomial equations was developed and implemented for the complex case. The geometry of polar varieties allows to extend this algorithm to a method for the characterization of the real solution set of systems of polynomial equations. The aim of this work is to apply an implementation of this method for the determination of real solutions to a class of example problems. Special emphasis was placed on the fact that those example problems possess real-life, practical applications. This requirement is satisfied for the systems of polynomial equations that result from the design of fast wavelet transforms. The wavelet transforms considered here shall possess the practical important properties of symmetry and orthogonality. The specification of such a wavelet transform depends on a finite number of real parameters. Those parameters have to obey certain polynomial equations. In the literature published on this topic, only example problems with a finite solution set were presented. For the computation of those examples it was sufficient to solve quadratic equations in one or two variables. To characterize the set of real solutions of a system of polynomial equations it is a first step to find at least one point in each connected component. Already this is an intrinsically hard problem. It turns out that the algorithm of the TERA-project performes very well with this task and is able to solve a larger number of examples than the best known commercial polynomial solvers.

This thesis examines three decomposition techniques and their usability for economic and financial time series. The stock index DAX30 and the exchange rate from British pound to US dollar are used as representative economic time series. Additionally, autoregressive and conditional heteroscedastic simulations are analysed as benchmark processes to the real data. Discrete wavelet transform (DWT) uses wavelike functions to adapt the behaviour of time series on different time scales. The second method is the singular spectral analysis (SSA), which is applied to extract influential reconstructed modes. As a third algorithm, empirical mode decomposition (END) leads to intrinsic mode functions, who reflect the short and long term fluctuations of the time series. Some problems arise in the decomposition process, such as bleeding at the DWT method or mode mixing of multiple EMD mode functions. Conclusions to evaluate the predictability of the time series are drawn based on entropy - and recurrence - analysis. The cyclic behaviour of the decompositions is examined via the coefficient of variation, based on the instantaneous frequency. The results show rising predictability, especially on higher decomposition levels. The instantaneous frequency measure leads to low values for regular oscillatory cycles, irregular behaviour results in a high variation coefficient. The singular spectral analysis show frequency - stable cycles in the reconstructed modes, but represents the influences of the original time series worse than the other two methods, which show on the contrary very little frequency - stability in the extracted details.

This thesis examines three decomposition techniques and their usability for economic and financial time series. The stock index DAX30 and the exchange rate from British pound to US dollar are used as representative economic time series. Additionally, autoregressive and conditional heteroscedastic simulations are analysed as benchmark processes to the real data. Discrete wavelet transform (DWT) uses wavelike functions to adapt the behaviour of time series on different time scales. The second method is the singular spectral analysis (SSA), which is applied to extract influential reconstructed modes. As a third algorithm, empirical mode decomposition (END) leads to intrinsic mode functions, who reflect the short and long term fluctuations of the time series. Some problems arise in the decomposition process, such as bleeding at the DWT method or mode mixing of multiple EMD mode functions. Conclusions to evaluate the predictability of the time series are drawn based on entropy - and recurrence - analysis. The cyclic behaviour of the decompositions is examined via the coefficient of variation, based on the instantaneous frequency. The results show rising predictability, especially on higher decomposition levels. The instantaneous frequency measure leads to low values for regular oscillatory cycles, irregular behaviour results in a high variation coefficient. The singular spectral analysis show frequency - stable cycles in the reconstructed modes, but represents the influences of the original time series worse than the other two methods, which show on the contrary very little frequency - stability in the extracted details.:1. Einleitung 2. Datengrundlage 2.1. Auswahl und Besonderheiten ökonomischer Zeitreihen 2.2. Simulationsstudie mittels AR-Prozessen 2.3. Simulationsstudie mittels GARCH-Prozessen 3. Zerlegung mittels modernen Techniken der Zeitreihenanalyse 3.1. Diskrete Wavelet Transformation 3.2. Singulärsystemanalyse 3.3. Empirische Modenzerlegung 4. Bewertung der Vorhersagbarkeit 4.1. Entropien als Maß der Kurzzeit-Vorhersagbarkeit 4.2. Rekurrenzanalyse 4.3. Frequenzstabilität der Zerlegung 5. Durchführung und Interpretation der Ergebnisse 5.1. Visuelle Interpretation der Zerlegungen 5.2. Beurteilung mittels Charakteristika 6. Fazit