Non-Markovian memory-based modelling of near- and far-from-equilibrium dynamical systems
Time series characterize diverse systems, examples in this proposal are: i) Proton motion in an inhomogeneous aqueous environment, ii) folding and unfolding of a peptide described by a suitably chosen reaction coordinate, iii) migration of a living cell on a substrate, iv) US Dollar / Yen exchange rate. Examples i) and ii) are close-to-equilibrium, iii) is a far from equilibrium since energy is constantly dissipated, while example iv) at first sight defies the classification into equilibrium or non-equilibrium.
For the understanding, comparison, classification and forecasting of time series data, stochastic differential equations, diverse random walk models, and more recently, machine-learning algorithms are commonly used. But fundamental questions remain unanswered: Is a unified description of such diverse systems possible? What is the relation between different proposed models? Can the non-equilibrium degree of a time series be estimated? 
NoMaMemo provides a unified description of generic time series data in terms of non-linear integro-differential stochastic equations based on memory functions that are extracted from data. NoMaMemo accounts for non-linear and non-equilibrium effects as well as for non-Gaussian noise and connects with fundamental concepts such as equilibrium statistical mechanics, response theory and entropy production. The general formulation contains previously proposed models and thus allows their comparison, forecasting quality will be compared with modern machine-learning algorithms. NoMaMemo creates a generic platform to analyse, understand, compare, classify and predict time series data and to optimize stochastic systems with respect to search efficiency, barrier-crossing speed or other figures of merit. NoMaMemo will significantly advance the understanding of chemical reaction and protein folding kinetics, the interpretation of THz and IR spectroscopy of liquids and the analysis of living matter and socio-economic data.