  Diffusion processes are important in several physical, chemical, biological
and human phenomena. Examples include molecular encounters in reactions,
cellular signalling, the foraging of animals, the spread of diseases, as well
as trends in financial markets and climate records. Deviations from Brownian
diffusion, known as anomalous diffusion, can often be observed in these
processes, when the growth of the mean square displacement in time is not
linear. An ever-increasing number of methods has thus appeared to characterize
anomalous diffusion trajectories based on classical statistics or machine
learning approaches. Yet, characterization of anomalous diffusion remains
challenging to date as testified by the launch of the Anomalous Diffusion
(AnDi) Challenge in March 2020 to assess and compare new and pre-existing
methods on three different aspects of the problem: the inference of the
anomalous diffusion exponent, the classification of the diffusion model, and
the segmentation of trajectories. Here, we introduce a novel method (CONDOR)
which combines feature engineering based on classical statistics with
supervised deep learning to efficiently identify the underlying anomalous
diffusion model with high accuracy and infer its exponent with a small mean
absolute error in single 1D, 2D and 3D trajectories corrupted by localization
noise. Finally, we extend our method to the segmentation of trajectories where
the diffusion model and/or its anomalous exponent vary in time.
