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.

翻译：学习时间序列模型对于仿真和预测等诸多应用具有重要价值。本研究考虑在满足给定安全约束的条件下主动学习时间序列模型的问题。我们采用具有非线性外生输入结构的高斯过程进行时间序列建模。所提出的方法通过动态探索输入空间，生成适用于时间序列模型学习的数据（即输入和输出轨迹）。该方法将输入轨迹参数化为连续的轨迹片段，这些片段根据安全需求和历史观测逐步确定。我们对所提算法进行了理论分析，并在一个技术应用场景中对其进行了实验评估。结果表明，我们的方法在实际技术案例中具有显著有效性。