The analysis of the immensity of data in space and time is a challenging task. For this thesis, the time-dependent data has been explored in various directions. The studies focused on data visualization, feature extraction, and data classification. The data that has been used in the studies comes from various well-recognized archives and has been the basis of numerous researches. The data characteristics ranged from the univariate time series to multivariate time series, from hand gestures to unconstrained views of general human movements. The experiments covered more than one hundred datasets. In addition, we also discussed the applications of visual analytics to video data. Two approaches were proposed to create a feature vector for time-dependent data classification. One is designed especially for a bio-inspired model for human motion recognition and the other is a subspace-based approach for arbitrary data characteristics. The extracted feature vectors of the proposed approaches can be easily visualized in two-dimensional space. For the classification, we experimented with various known models and offered a simple model using data in subspaces for light-weight computation. Furthermore, this method allows a data analyst to inspect feature vectors and detect an anomaly from a large collection of data simultaneously. Various classification techniques were compared and the findings were summarized. Hence, the studies can assist a researcher in picking an appropriate technique when setting up a corresponding model for a given characteristic of temporal data, and offer a new perspective for analyzing the time series data. This thesis is comprised of two parts. The first part gives an overview of time-dependent data and of this thesis with its focus on classification; the second part covers the collection of seven publications.