1. A method including: performing ensemble learning of an ensemble of classifiers or regressors to predict a non-stationary time-varying parameter over a time series of episodes, each episode comprising a time period; wherein the ensemble learning includes for an episode of the time series of episodes: selecting an ensemble action from a set of ensemble actions based on ensemble action quality values (Q values) for the ensemble actions at an error state for the episode that is indicative of error of the ensemble of classifiers or regressors in predicting the non-stationary time-varying parameter, executing the selected ensemble action to update the ensemble of classifiers or regressors, computing or retrieving a cost of executing the selected ensemble action, computing a reward indicative of how well the updated ensemble of classifiers or regressors predicts the non-stationary time-varying parameter over the episode, and updating the Q value for the selected ensemble action at the error state for the episode based on both the reward and the cost of executing the selected ensemble action; wherein performing of ensemble learning is performed by an electronic data processing device programmed to perform the ensemble learning.