Earth system modelling (ESM) is essential for understanding past, present and future Earth
processes. Deep learning (DL), with the data-driven strength of neural networks, has …

In the last decade, there has been a growing body of literature addressing the utilization of
complex network methods for the characterization of dynamical systems based on time …

The role of seasonality is indisputable in climate and ecosystem dynamics. Seasonal
temperature and precipitation variability are of vital importance for the availability of food …

Specific components of the Earth system may abruptly change their state in response to
gradual changes in forcing. This possibility has attracted great scientific interest in recent …

How different are the results of constant-rate resetting of anomalous-diffusion processes in
terms of their ensemble-averaged versus time-averaged mean-squared displacements …

The last decade has witnessed a number of important and exciting developments that had
been achieved for improving recurrence plot-based data analysis and to widen its …

Establishing a carbon market is widely regarded as an effective means of controlling global
carbon emission. Purchasing carbon emission right will increase the cost of enterprises …

Carbon price prediction is a key issue in the field of carbon market research. However, the
existing methods of carbon price forecasting mostly regard carbon price series as a certain …

Nonlinear time series analysis gained prominence from the late 1980s on, primarily because
of its ability to characterize, analyze, and predict nontrivial features in data sets that stem …

We study continuous-time reinforcement learning (RL) for stochastic control in which system
dynamics are governed by jump-diffusion processes. We formulate an entropy-regularized …