Deep Attentive Time Warping
Shinnosuke Matsuo Xiaomeng Wu Gantugs Atarsaikhan Akisato Kimura Kunio Kashino Brian Kenji Iwana Seiichi Uchida
Similarity measures for time series are important problems for time series
classification. To handle the nonlinear time distortions, Dynamic Time Warping
(DTW) has been widely used. However, DTW is not learnable and suffers from a
trade-off between robustness against time distortion and discriminative power.
In this paper, we propose a neural network model for task-adaptive time
warping. Specifically, we use the attention model, called the bipartite
attention model, to develop an explicit time warping mechanism with greater
distortion invariance. Unlike other learnable models using DTW for warping, our
model predicts all local correspondences between two time series and is trained
based on metric learning, which enables it to learn the optimal data-dependent
warping for the target task. We also propose to induce pre-training of our
model by DTW to improve the discriminative power. Extensive experiments
demonstrate the superior effectiveness of our model over DTW and its
state-of-the-art performance in online signature verification.