  Recurrent auto-encoder model summarises sequential data through an encoder
structure into a fixed-length vector and then reconstructs the original
sequence through the decoder structure. The summarised vector can be used to
represent time series features. In this paper, we propose relaxing the
dimensionality of the decoder output so that it performs partial
reconstruction. The fixed-length vector therefore represents features in the
selected dimensions only. In addition, we propose using rolling fixed window
approach to generate training samples from unbounded time series data. The
change of time series features over time can be summarised as a smooth
trajectory path. The fixed-length vectors are further analysed using additional
visualisation and unsupervised clustering techniques. The proposed method can
be applied in large-scale industrial processes for sensors signal analysis
purpose, where clusters of the vector representations can reflect the operating
states of the industrial system.
