Switching Probabilistic Slow Feature Analysis for Time Series Data
Kazuki Tsujimoto, Toshiaki Omori · International Journal of Machine Learning and Computing · 2020
Slow feature analysis (SFA) is a machine learning method for extracting slowly time-varying feature from multidimensional time series data.Recently, probabilistic SFA (PSFA) that extends SFA to a probabilistic framework has been proposed.The PSFA can be applied to stationary time series data with noise and missing values.In order to deal with nonstationary time series data including change points, we propose a switching probabilistic slow feature analysis (switching PSFA) in this paper.By introducing a switching state space model, it is possible to extract slowly varying information even when system parameters change with time.Using the proposed method, we show that slowly time-varying components can be extracted more accurately from time-series data with non-stationarity.