Inference of a time delay in stochastic systems

Time delay is ubiquitous in many experimental and real-world situations. However, from measurable data it is often unclear whether time delay plays a significant role in the dynamics, and if it does, how long the time lag really is. This would be invaluable knowledge when analyzing and modeling such systems. Hitherto, no universal method is available by which the time delay can be inferred. To address this problem, we propose and demonstrate two different methods to infer time delay in overdamped Langevin systems with delayed feedback. In the first part, we focus on the power spectral density based on the positional data and use a characteristic signature of the time delay to infer the delay time. In limiting cases, we establish a direct relation between the observations made for nonlinear time-delayed feedback forces and analytical results obtained for the linear system. In other situations, despite the absence of this direct relation, the characteristic signature remains and can be exploited by a semiautomatic method to infer the delay time. Furthermore, it may not always desirable or possible to observe a system for a long time to infer dependencies and parameters. Thus, in the second part, we propose a probing method combined with a neural network to infer the delay time, which requires only short observation time series. These proposed methods for inferring time delays in stochastic systems can be valuable tools for gaining deeper insight into the role of delay across a wide range of applications—from the behavior of individual colloidal particles under feedback control to emergent collective phenomena such as flocking and swarming.