Abstractions of signals help to avoid cognitive and storage overload, especially in systems with many signals such as cyber-physical systems (CPS). For assessing trends and reconstructing behavior, it often suffices to have a rough understanding how signals evolve, e.g., whether their behavior is monotonic or periodic. This work provides a configurable abstraction of signal behavior, where signals are described by a sequence of oscillation and linear patterns. We formalize templates of both behaviors in parameterized signal temporal logic (PSTL) and provide an algorithm that abstracts signals in terms of those patterns. The templates are configurable such that they allow for choosing the level of abstraction, e.g., by limiting the approximation error or the minimal oscillation frequency. For segmentation of the signal, we solve an optimization problem using a modified version of TeLEx. The evaluation demonstrates that configuration of the patterns is suitable to define the level of abstraction. Further, on control output from the ARCH wind turbine benchmark and flow data from medical ventilation, it demonstrates that the abstraction method can be applied to real-world signals.