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 fixedlength 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.
The paper describes a sequence to sequence auto-encoder model which is used to learn sequence representations. The authors show that for their application, better performance is obtained when the network is only trained to reconstruct a subset of the data measurements. The paper also presents some visualizations the similarity structure of the learned representations and proposes a window-based method for processing the data.
According to the paper, the experiments are done using a data set which is obtained from measurements of an industrial production process. Figure 2 indicates that reconstructing fewer dimensions of this dataset leads to lower MSE scores. I don’t see how this is showing anything besides the obvious fact that reconstructing fewer dimensions is an easier task than reconstructing all of them.  The only conclusions I can draw from the visual analysis is that the context vectors are more similar to each other when they are obtained from time steps in the data stream which are close to each other. Since the paper doesn’t describe much about the privately owned data at all, there is no possibility to replicate the work. The paper doesn’t frame the work in prior research at all and the six papers it cites are only referred to in the context of describing the architecture.
I found it very hard to distil what the main contribution of this work was according to the paper. There were also not many details about the precise architecture used. It is implied that GRU networks and were used but the text doesn’t actually state this explicitly. By saying so little about the data that was used, it was also not clear what the temporal correlations of the context vectors are supposed to tell us. 
The paper describes how existing methods are applied to a specific data set. The benefit of only reconstructing a subset of the input dimensions seems very data specific to me and I find it hard to consider this a novel idea by itself. Presenting sequential data in a windowed format is a standard procedure and not a new idea either. All in all I don't think that the paper presents any new ideas or interesting results.
Pros:
* The visualizations look nice.
Cons:
* It is not clear what the main contribution is.
* Very little information about the data. 
* No clear experiments from which conclusions can be drawn.
* No new ideas.
* Not well rooted in prior work.