Machine learning is the art of generalizing a set of examples. Beside the efficiency of the algorithms, the challenge is to define generalizations that make sense for a data scientist. In this article, we consider generalizations of temporal sequences as chronicles. A chronicle is a temporal model that represents a situation occurring in temporal sequences, i.e. a series of event types with timestamps. A chronicle is a collection of event types with metric temporal constraints on their delays of occurrence. Generalizing sequences by a set of event types can intuitively be the smallest set of events that occur in all sequences. A question arises with the generalization of metric temporal constraints. In the article, we study the admissibility of these generalizations by deriving the notion of rule admissibility to the generalization as chronicles. Through formalization, new insights about the notions of chronicles may lead to conceive original chronicle mining algorithms.