  Traditional techniques for measuring similarities between time series are
based on handcrafted similarity measures, whereas more recent learning-based
approaches cannot exploit external supervision. We combine ideas from
time-series modeling and metric learning, and study siamese recurrent networks
(SRNs) that minimize a classification loss to learn a good similarity measure
between time series. Specifically, our approach learns a vectorial
representation for each time series in such a way that similar time series are
modeled by similar representations, and dissimilar time series by dissimilar
representations. Because it is a similarity prediction models, SRNs are
particularly well-suited to challenging scenarios such as signature
recognition, in which each person is a separate class and very few examples per
class are available. We demonstrate the potential merits of SRNs in
within-domain and out-of-domain classification experiments and in one-shot
learning experiments on tasks such as signature, voice, and sign language
recognition.
