Talk, Hong Kong Machine Learning Meetup, Hong Kong
Deep learning models can be used to extract representations for multidimensional time series data. We have used a sensors dataset collected from a large-scale industrial facility to illustrate this problem. Real-values sensor signals were treated as multidimensional time series and fed through a recurrent auto-encoder model. Representations extracted can be projected to low dimensionality space and reflect temporal behaviour of the underlying time series. In this way, 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.