Modeling, learning, and meaning: extracting regimes from time series

Many real-world time series are multistationary, where the underlying data generating process switches between different stationary subprocesses, or modes of operation. An important problem in modeling such systems is to discover the underlying switching process, which entails identifying the number of subprocesses and the dynamics of each subprocess. For many time series, this problem is ill-defined, since there are often no obvious means to distinguish the different subprocesses. We discuss the use of nonlinear gated experts to perform the segmentation and system identification of the time series. Unlike standard gated experts methods, however, we modify the training algorithm to enhance the segmentation for high-noise problems where only a few experts are required