Regime searching in time series data using Variational Autoencoder
А. В. Просветов · Journal of Physics Conference Series · 2021
Abstract In the current work we propose a method to extract regimes from time series using unsupervised learning. The proposed method is based on neural network with architecture of variational autoencoder and clusterization in latent space. The method has been proven by extracting regimes from a steam turbine telemetry data set and from human activity recognition data, which suggests that the proposed approach can extract regimes from time series data obtained for different areas.