Cardiovascular diseases are one of the leading causes for morbidity and mortality. It is therefore of crucial importance to identify indicators for these diseases at an early stage to find proper treatment, prevent fatal outcome and launch preventive actions. There are many parameters describing the health condition of the cardiovascular system, the most popular being systolic and diastolic blood pressure (BP). Nevertheless, hypertension is only able to predict 40% of coronary heart diseases. Therefore, further indicators have to be found. The Mobil-O-Graph (I.E.M., Stolberg, Germany) is an oscillometric brachial-cuff based sphygmomanometer which allows to perform 24 hour (24h) ambulatory blood pressure monitoring (ABPM) including pulse wave analysis (PWA). The recording involves the measurement of standard ABPM parameters as well as the estimation of central aortic pressures and other systemic cardiovascular parameters, such as augmentation index (AIx) and cardiac output (CO), at regular time intervals throughout the day. The resulting time series often show a diurnal profile. Therefore, the analysis of these profiles and their variability is of interest in the field of biomedical engineering and medical pathophysiology. The aim of this thesis is to identify suitable mathematical models and indices to quantify this profile and the variability of the time series. Furthermore, algorithms which are applicable to the data sets and provide these indices, need to be implemented. In this context, the analysis of diurnal BP profiles serves as a model. In this thesis, the methods used in literature to assess blood pressure variability (BPV) are researched and documented in detail. These methods, which have been used in clinical studies for 24h BP profiles for considerable time, are adopted for other parameters of the PWA in order to mathematically quantify the variability of a time series regardless of the parameter. Additionally, other indices, which have not yet been analysed at all in the context of BP and PWA parameter, but are rather general measures of variation within a time series, are presented. The considered methods include simple variability indices, such as the standard deviation (SD), average real variability (ARV), successive variation (SV) and the coefficient of variation (CV). Other methods aim to assess the diurnal profile of the parameter time series. In general, this is achieved by curve fitting methods. An ansatz function of a specific from is fitted to the data set by a least squared error criterion. One of the most popular models is the fourier fit. The sum of cosine waves with different period lengths builds the ansatz function. If only one cosine wave with a period of 24h is used, the method is called cosinor fit. Another model, the square-wave (SW) fit, assumes that the profile can be described by two constant plateaux. These and further models provide indices quantifying the profile. There are also simple indices which capture certain aspects of the profile. The nocturnal fall (NF), for instance, quantifies to what extent values at night differ from day measurements on average. As a next step, the methods are implemented in MATLAB (The MathWorks Inc., Natick, Massachusetts, USA) to allow the application of the approaches on data sets recorded by the Mobil-O-Graph. Some of the methods impose conditions on the data sets to be computable, such as a minimum number of valid recordings in a given time period. Therefore, an algorithm is created to test data sets for the required quality. The variability indices provided by the methods are calculated for a selection of the ABPM and PWA parameters of a healthy population as well as a patient group suspected to suffer from left ventricular hypertrophy (LVH) in order to test them for significant differences. Beforehand, data sets of insufficient quality are excluded with the help of the above mentioned quality algorithm. The demanded quality is thereby based on common settings in literature. The parameters analysed are heart frequency (Hf), peripheral systolic blood pressure (pSBP), central systolic blood pressure (cSBP), peripheral pulse pressure (pPP), central pulse pressure (cPP) andAIx. The results show that pSBP, cSBP and AIx have significantly different 24h average values among the two cohorts. In contrast, Hf as well as the PP average values are not statistically different for the two groups. However, for Hf, several variability indices provide statistically different values, among them SD, ARV and SV. For each of the PP parameters only one index is significantly different - the NF for cPP and the CV at night for pPP. Even if pSBP, cSBP and AIx have 24h average values, which have a statistical difference, additional information might be obtained by the variability indices. The NF and the indices of the cosinor fit are significantly different for cSBP as well as pSBP. Indices of several methods, for instance, SD, ARV and NF are significantly different for the AIx. The large amount of indices gives a wide-ranging number of aspects to be considered. Even if not all of the PWA parameters have been analysed in the frame of this work, the findings are of interest in the context of identifying indices with possible prognostic relevance. The mathematical models prove to be adequate to assess the diurnal profile and variability of 24h PWA parameters and the implemented algorithms are feasible to be applied to the data sets. This enables further investigation of clinical questions based on variability analysis.