RTFN: Robust Temporal Feature Network
This work addresses a bottleneck in time series analysis for applications like healthcare and weather prediction, offering incremental improvements in feature extraction methods.
The paper tackles the challenge of extracting sufficient shapelets in time series analysis by proposing RTFN, a robust temporal feature network that combines temporal feature networks and attentional LSTM networks, achieving state-of-the-art results with wins on 40 out of 85 datasets in supervised tasks and best performance on 4 out of 11 datasets in unsupervised clustering.
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.