This thesis explores the feasibility of clustering mixed data and unevenly spaced time series for customer segmentation. The proposed method implements the Gower dissimilarity as the local distance function in dynamic time warping to calculate dissimilarities between mixed data time series. The time series are then clustered with k−medoids and the clusters are evaluated with the silhouette score and t−SNE. The study further investigates the use of a time warping regularisation parameter. It is derived that implementing time as a feature has the same effect as penalising time warping, andtherefore time is implemented as a feature where the feature weight is equivalent to a regularisation parameter. The results show that the proposed method successfully identifies clusters in customer transaction data provided by Nordea. Furthermore, the results show a decrease in the silhouette score with an increase in the regularisation parameter, suggesting that the time at which a transaction occurred might not be of relevance to the given dataset. However, due to the method’s high computational complexity, it is limited to relatively small datasets and therefore a need exists for a more scalable and efficient clustering technique. / Denna uppsats utforskar klustring av ojämnt fördelade tidsserier med numeriska och kategoriska variabler för kundsegmentering. Den föreslagna metoden implementerar Gower dissimilaritet som avståndsfunktionen i dynamic time warping för att beräkna dissimilaritet mellan tidsserierna. Tidsserierna klustras sedan med k-medoids och klustren utvärderas med silhouette score och t-SNE. Studien undersökte vidare användningen av en regulariserings parameter. Det härledes att implementering av tid som en egenskap hade samma effekt som att bestraffa dynamic time warping, och därför implementerades tid som en egenskap där dess vikt är ekvivalent med en regulariseringsparameter.  Resultaten visade att den föreslagna metoden lyckades identifiera kluster i transaktionsdata från Nordea. Vidare visades det att silhouette score minskade då regulariseringsparametern ökade, vilket antyder att tiden transaktion då en transaktion sker inte är relevant för det givna datan. Det visade sig ytterligare att metoden är begränsad till reltaivt små dataset på grund av dess höga beräkningskomplexitet, och därför finns det behov av att utforksa en mer skalbar och effektiv klusteringsteknik.

This thesis, structured in two parts, addresses a series of problems of relevance in the field of Spatial Geodesy. The first part delves into the application of satellite gravity data to enhance our understanding of water transport dynamics. Here, we present two significant contributions. Both are based on satellite gravity data but stem from different mission concepts with distinct objectives: time-variable gravity monitoring and high-resolution, accurate static geoid modelling. First, the fundamental notions about gravity are introduced and a brief summary is made of the different gravity satellite missions throughout history, with emphasis on the GRACE/GRACE-FO and GOCE missions, whose data are the basis of this work. The first application focuses on estimating water transport and geostrophic circulation in the Southern Ocean by leveraging a GOCE geoid and altimetry data. The Volume Transport across the Antartic Circumpolar Current is analyzed and the resulsts are validated validated using the in-situ data collected during the multiple campaigns in the DP. The second application uses time-variable gravity data from the GRACE and GRACE-FO missions to estimate the water cycle in the Mediterranean and Black Sea system, a critical region for regional climate and global ocean circulation. The analysis delves into the analysis of the different components of the hydrological cycle within this region, including the water flow across the Gibraltar Strait, examining their seasonal variations, climatic patterns, and their connection with the North Atlantic Oscillation Index. The second part of the thesis is more focused on data analysis, with the objective of developing mathematical methods to estimate the cross correlation function between two time series that are both unevenly spaced spaced (the sampling is not uniform over time) and observed at unequal time scales (the set of time points for the first series is not identical to the set of time points of the second series). Such time series are frequently encountered in geodetic surveys, especially when combining data from different sources. The estimation of the the cross correlation function for these time series presents unique challenges and requires the adaptation of traditional analysis methods designed for evenly spaced and synchronized time series. The two main contributions in this context are: (i) the study of the asymptotic properties of the Guassian Kernel estimator, that is the recommended estimator for the cross correlation function when the two time series are observed at unequal time scales; (ii) an extension of the stationary bootstrap that allows to construct bootstrap-based confidence intervals for the cross correlation function for unevenly spaced time series not sampled on identical time points.