We propose a novel Bayesian approach to modelling nonlinear alignments of
time series based on latent shared information. We apply the method to the
real-world problem of finding common structure in the sensor data of wind
turbines introduced by the underlying latent and turbulent wind field. The
proposed model allows for both arbitrary alignments of the inputs and
non-parametric output warpings to transform the observations. This gives rise
to multiple deep Gaussian process models connected via latent generating
processes. We present an efficient variational approximation based on nested
variational compression and show how the model can be used to extract shared
information between dependent time series, recovering an interpretable
functional decomposition of the learning problem. We show results for an
artificial data set and real-world data of two wind turbines.

本研究提出了一种基于潜在的共享信息的贝叶斯方法，用于建模时间序列的非线性对齐，并将该方法应用于传感器数据中寻找风力涡轮机数据中的共同结构。提出的模型允许输入的任意对齐和非参数输出扭曲以转换观察结果。