Recurrent auto-encoder model can summarise sequential data through an encoder structure into a fixed-length vector and then reconstruct into its original sequential form through the decoder structure. The summarised information 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 can therefore represent features only in the selected dimensions. In addition, we propose using rolling fixed window approach to generate samples. The change of time series features over time can be summarised as a smooth trajectory path. The fixed-length vectors are further analysed through additional visualisation and unsupervised clustering techniques. 

 This proposed method can be applied in large-scale industrial processes for sensors signal analysis purpose where clusters of the vector representations can be used to reflect the operating states of selected aspects of the industrial system.
Using recurrent auto-encoder model to extract multidimensional time series features This writeup describes an application of recurrent autoencoder to analyze of multidimensional time series The paper describes a sequence to sequence auto-encoder model which is used to learn sequence representations, showing that for their application, better performance is obtained when the network is only trained to reconstruct a subset of the data measurements.  Proposes a strategy inspired by the recurrent auto-encoder model such that clustering multidimensional time series data can be performed based on context vectors.