  The study of the dynamics of natural and artificial systems has provided
several examples of deviations from Brownian behavior, generally defined as
anomalous diffusion. The investigation of these dynamics can provide a better
understanding of diffusing objects and their surrounding media, but a
quantitative characterization from individual trajectories is often
challenging. Efforts devoted to improving anomalous diffusion detection using
classical statistics and machine learning have produced several new methods.
Recently, the anomalous diffusion challenge (AnDi,
https://www.andi-challenge.org) was launched to objectively assess these
approaches on a common dataset, focusing on three aspects of anomalous
diffusion: the inference of the anomalous diffusion exponent; the
classification of the diffusion model; and the segmentation of trajectories. In
this article, I describe a simple approach to tackle the tasks of the AnDi
challenge by combining extreme learning machine and feature engineering
(AnDi-ELM). The method reaches satisfactory performance while offering a
straightforward implementation and fast training time with limited computing
resources, making a suitable tool for fast preliminary screening.
