Safe Active Learning for Time-Series Modeling with Gaussian Processes
Christoph Zimmer Mona Meister Duy Nguyen-Tuong
Learning time-series models is useful for many applications, such as
simulation and forecasting. In this study, we consider the problem of actively
learning time-series models while taking given safety constraints into account.
For time-series modeling we employ a Gaussian process with a nonlinear
exogenous input structure. The proposed approach generates data appropriate for
time series model learning, i.e. input and output trajectories, by dynamically
exploring the input space. The approach parametrizes the input trajectory as
consecutive trajectory sections, which are determined stepwise given safety
requirements and past observations. We analyze the proposed algorithm and
evaluate it empirically on a technical application. The results show the
effectiveness of our approach in a realistic technical use case.