Autowarp: Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders
This addresses the challenge for domain experts in fields like medicine and finance who rely on manually selected metrics for time series analysis, offering an automated and interpretable solution.
The paper tackles the problem of measuring similarities between unlabeled time series trajectories by proposing Autowarp, an end-to-end algorithm that learns a warping metric from data, which often outperforms hand-crafted metrics in experiments across domains.
Measuring similarities between unlabeled time series trajectories is an important problem in domains as diverse as medicine, astronomy, finance, and computer vision. It is often unclear what is the appropriate metric to use because of the complex nature of noise in the trajectories (e.g. different sampling rates or outliers). Domain experts typically hand-craft or manually select a specific metric, such as dynamic time warping (DTW), to apply on their data. In this paper, we propose Autowarp, an end-to-end algorithm that optimizes and learns a good metric given unlabeled trajectories. We define a flexible and differentiable family of warping metrics, which encompasses common metrics such as DTW, Euclidean, and edit distance. Autowarp then leverages the representation power of sequence autoencoders to optimize for a member of this warping distance family. The output is a metric which is easy to interpret and can be robustly learned from relatively few trajectories. In systematic experiments across different domains, we show that Autowarp often outperforms hand-crafted trajectory similarity metrics.