论文标题
RTFN：强大的时间功能网络
RTFN: Robust Temporal Feature Network
论文作者
论文摘要
时间序列分析在各种应用中起着至关重要的作用，例如医疗保健，天气预测，灾难预测等。但是，通过功能网络获得足够的形状仍然具有挑战性。为此，我们提出了一个新颖的鲁棒时间特征网络（RTFN），其中包含时间特征网络和注意力LSTM网络。构建时间功能网络是为了从输入数据中提取基本功能，而注意力LSTM网络则被设计为捕获复杂的形状和关系以丰富功能。在实验中，我们分别将RTFN嵌入了监督的结构中，分别作为特征提取网络，并分别作为编码器中的无监督聚类。结果表明，基于RTFN的监督结构是85个数据集中40个赢家，而基于RTFN的无监督聚类在UCR2018 Archive的11个数据集中的4分中表现最好。
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.
