Analysis and modeling of time series (TS) data plays an important role in monitoring and control tasks related to large and/or complex systems, such as electric power grids. Typically, such time series exhibit nested temporal cycles (e.g., hours, days, weeks) inherent to human activities. We here present a TS modeling approach that makes explicit use of this inherent cyclicity for the purpose of providing appropriate prediction of time-dependent parameters for situation assessment and decision support. The approach is part of ongoing research towards providing Visual Analytics support in the context of critical infrastructure monitoring.