  Time series analysis plays a vital role in various applications, for
instance, healthcare, weather prediction, disaster forecast, etc. However, to
obtain sufficient shapelets by a feature network is still challenging. To this
end, we propose a novel robust temporal feature network (RTFN) that contains
temporal feature networks and attentional LSTM networks. The temporal feature
networks are built to extract basic features from input data while the
attentional LSTM networks are devised to capture complicated shapelets and
relationships to enrich features. In experiments, we embed RTFN into supervised
structure as a feature extraction network and into unsupervised clustering as
an encoder, respectively. The results show that the RTFN-based supervised
structure is a winner of 40 out of 85 datasets and the RTFN-based unsupervised
clustering performs the best on 4 out of 11 datasets in the UCR2018 archive.
