This paper outlines a general framework for automatic selection in multi-scale representations of temporal and spatio-temporal data, A general principle for automatic scale selection based on local maxima of normalized differential entities is adapted to the temporal domain, and it is shown how the notion of normalized derivatives can be defined for three main types of (continuous and discrete) temporal scale-space representations. Closed-form analysis is carried out for basic model patterns, and shows how the suggested theory applies to motion detection and motion estimation.