2. Unsupervised feature extraction

In principle, any low-dimensional representation of the time series data would constitute a feature set, that is, the data in the time series window, X ∈ R<sup>N</sup>x<sup>M</sup> containing N measurements of the M plant variables with time lagged copies of

these variables. These features can subsequently be dealt with by the same methods used for steady state systems, such as principal component analysis, independent component analysis, kernel methods, etc., some of which are considered in more detail below.
