Data as Demonstrator (DaD) is a meta learning algorithm to improve the multi-step predictive capabilities of a learned time series (e.g. dynamical system) model.
Thanks for your excellent work! I just have a simple question, for some time series forecasting problems, e.g., predict the temperature in New York City, we only have one trajectory of the realization of data, unlike your example which could have as many as you want. In this scenario, is there any special considerations before using your algorithms?