Time series analysis has achieved great success in diverse applications such
as network security, environmental monitoring, and medical informatics.
Learning similarities among different time series is a crucial problem since it
serves as the foundation for downstream analysis such as clustering and anomaly
detection. It often remains unclear what kind of distance metric is suitable
for similarity learning due to the complex temporal dynamics of the time series
generated from event-triggered sensing, which is common in diverse
applications, including automated driving, interactive healthcare, and smart
home automation. The overarching goal of this paper is to develop an
unsupervised learning framework that is capable of learning task-aware
similarities among unlabeled event-triggered time series. From the machine
learning vantage point, the proposed framework harnesses the power of both
hierarchical multi-scale sequence autoencoders and Gaussian Mixture Model (GMM)
to effectively learn the low-dimensional representations from the time series.
Finally, the obtained similarity measure can be easily visualized for
explaining. The proposed framework aspires to offer a stepping stone that gives
rise to a systematic approach to model and learn similarities among a multitude
of event-triggered time series. Through extensive qualitative and quantitative
experiments, it is revealed that the proposed method outperforms
state-of-the-art methods considerably.

该研究旨在开发一种无监督学习框架，能够在未标记的事件触发时间序列中学习任务感知的相似性，该框架利用多尺度自编码器和高斯混合模型有效地学习时间序列的低维表示，得到的相似度量易于解释，通过广泛的实验表明，该方法明显优于现有的方法。

事件触发时间序列的任务感知相似度学习

Task-aware Similarity Learning for Event-triggered Time Series

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.

本研究提出了一种名为 Autowarp 的算法，它利用序列自编码器优化学习特定的度量准则来衡量不加标签的时间序列轨迹之间的相似性，并在实验中表现良好。