Training Strategies for Time Series: Learning for Prediction, Filtering, and Reinforcement Learning

A new training algorithm for learned time-series models, Data as Demonstrator (DaD), is introduced that theoretically and empirically improves multi-step prediction performance on model classes such as recurrent neural networks, kernel regressors, and random forests and can accelerate model-based reinforcement learning.