Today, time series of numerical data are ubiquitous, for instance in the Internet of Things. In such scenarios, it is often necessary to compress the data and to detect changes on it. More specifically, both methods are used in combination, i.e., data is lossily compressed and later decompressed, and then change detection takes place. There exists a broad variety of compression as well as of change-detection techniques. This calls for a systematic comparison of different combinations of compression and change-detection techniques, for different data sets, together with recommendations on how the values of the various (typically non-linear) parameters should be chosen. This article is such an evaluation. Its design is not trivial, necessitating a number of decisions. We work out the details and the rationale behind our design choices. Next to other results, our study shows that the choice of combinations of change detection and compression algorithm and their parameterization does affect result quality significantly. Our evaluation also indicates that results are highly contingent on the nature of the data.ers specify their privacy r ... mehrequirements as so-called discriminative pairs of secrets, and it perturbs data so that an adversary does not learn about the probability distribution of such pairs. However, deploying the framework on complex data such as time series requires application specific work. This includes a general definition of the representation of secrets in the data. Another issue is that the tradeoff between Pufferfish privacy and utility of the data is largely unexplored in quantitative terms. In this study, we quantify this tradeoff for smart meter data. Such data contains fine-grained time series of power-consumption data from private households. Disseminating such data in an uncontrolled way puts privacy at risk. We investigate how time series of energy consumption data must be transformed to facilitate specifying secrets that Pufferfish can use. We ensure the generality of our study by looking at different information-extraction approaches, such as re-identification and non-intrusive-appliance-load monitoring, in combination with a comprehensive set of secrets. Additionally, we provide quantitative utility results for a real-world application, the so-called local energy market.