In this project, we propose the first method to predict ongoing activities over a hierarchical label space. We approach this task as a sequence prediction problem in a recurrent neural network where we predict over a hierarchical label space of activities. Our model learns to realize accuracy-specificity trade-offs over time by starting with coarse labels and proceeding to more fine grained recognition as more evidence becomes available in order to meet a prescribed target ac- curacy. In order to study this task we have collected a large video dataset of complex activities with long duration. The activities are annotated in a hierarchical label space from coarse to fine.