For any stream of time-stamped edges that form a dynamic network, an important choice is the aggregation granularity that an analyst uses to bin the data. Picking such a windowing of the data is often done by hand, or left up to the technology that is collecting the data. However, the choice can make a big difference in the properties of the dynamic network. Finding a good windowing is the time scale detection problem. In previous work, this problem is often solved with an unsupervised heuristic. As an unsupervised problem, it is difficult to measure how well a given windowing algorithm performs. In addition, we show that there is little correlation between the quality of a windowing across different tasks. Therefore the time scale detection problem should not be handled independently from the rest of the analysis of the network. Given this, in accordance with standard supervised machine learning practices, we introduce new windowing algorithms that automatically adapt to the task the analyst wants to perform by treating windowing as a hyperparameter for the task, rather than using heuristics. This approach measures the quality of the windowing by how well a given task is accomplished on the resulting network. This also allows us, for the first time, to directly compare different windowing algorithms to each other, by comparing how well the task is accomplished using that windowing algorithm. We compare this approach to previous approaches and several baselines on real data.