Deep Prototypical Networks for Imbalanced Time Series Classification under Data Scarcity

With the increase of temporal data availability, time series classification has drawn a lot of attention in the literature because of its wide spectrum of applications in diverse domains (e.g., healthcare, bioinformatics and finance), ranging from human activity recognition to financial pattern identification. While significant progress has been made to solve time series classification problem, the success of such methods relies on data sufficiency, and may not well capture the quality embeddings when training triple instances are scarce and highly imbalance across classes. To address these challenges, we propose a prototype embedding framework-Deep Prototypical Networks (DPN), which leverages a main embedding space to capture the discrepancies of different time series classes for alleviating data scarcity. In addition, we further augment DPN framework with a relationship-dependent masking module to automatically fuse relevant information with a distance metric learning process, which addresses the data imbalance issue and performs robust time series classification. Experimental results show significant and consistent improvements as compared to state-of-the-art techniques.