Before revision:  From emulous and modern graph collections to a highly topological synthetic dynamical orbits data, we prove that the method matches or beats the state-of-the-art in encoding persistence diagrams to solve hard problems. We then apply our method in the context of an industrial, difficult time-series regression problem and show the approach to be relevant .
After revision:  From emulous and modern graph collections to a highly topological synthetic dynamical orbits problem. The proposed methodology comes with only high level tuning parameters such as the total measure encoding budget, and we provide a completely open access software .