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. Successive context vectors generated by windowing approach are always highly correlated, thus form a smooth trajectory in high-dimensional space. Additional dimensionality reduction techniques can be applied visualise the change of features as a smooth trajectory. One of the key contributions of this study is that, the context vectors form distinct neighbourhoods which can be identified through unsupervised clustering algorithms such as K-means. The clusters can be optionally labelled manually to identify operating state (e.g. healthy vs. faulty). Alarm can be triggered when the context vector travels beyond the boundary of a predefined neighbourhood. Moreover, this enables us to find the clusters of unlabelled time series data. Clusters of the vector representation can be used by operators and engineers to aid diagnostics and maintenance.Another contribution of this study is that dimensionality of the output sequence can be relaxed, thus allowing the recurrent auto-encoder to perform partial reconstruction. Although it is clearly easier for the model to partially reconstruct the original sequence, such simple improvement allows users to define different sets of sensors of particular interest. By limiting the number of sensors to include in the output dimension, the context vector can be used to reflect the underlying states of specific aspects of the large-scale industrial process. This ultimately generates more actionable insights and enables users to diagnose the induatrial system. We have demonstrated the use of partial reconstruction by through two examples which graphically show the effects of it.This proposed method performs multidimensional time series clustering, which can natively scale up to very high dimensionality as it is based on recurrent auto-encoder model. We have applied the method to an industrial sensor dataset with P = 158 and empirically show that it can summarise multidimensional time series data effectively.The model can be generalised to any multi-sensor multi-state processes for operating state recognition. We also recognise that the cost of collecting labelled time series data can be very expensive. This study established that recurrent auto-encoder model can be used to analyse unlabelled and un-bounded time series data. This opens up further possibilities for analysing IoT and industrial sensors data given that these domains are predominately overwhelmed with unbounded and unlabelled time series data. Nevertheless, the proposed approach has not included any categorical sensor measurements (e.g. open/closed, tripped/healthy, start/stop... etc). Future research can focus on incorporating categorical measurements alongside real-valued measurements.
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.