While offline exploration of time series can be useful, time series analysis is almost unique in allowing the possibility of direct and immediate intervention. For example, if we are monitoring an industrial process and our algorithm predicts imminent failure, the algorithm could direct a controller to open a release valve or alert a response team. There now exist mature tools to monitor time series for known behaviors (template matching), previously unknown highly conserved behaviors (motifs) and unexpected behaviors (anomalies). In this work we claim that there is another useful primitive, emerging behaviors, that are worth monitoring for. We call such behaviors Novelets. We explain that Novelets are neither anomalies nor motifs but can be loosely thought of as initially apparent anomalies that are later realized to be motifs. We will show Novelets have a natural interpretation in many disciplines, including science, medicine, and industry. As we will further demonstrate, Novelet discovery can have many downstream uses, including prognostics and abnormal behavior detection. We will demonstrate the utility of our proposed primitive on a diverse set of domains.