Representation learning plays a critical role in the analysis of time series
data and has high practical value across a wide range of applications.
including trend analysis, time series data retrieval and forecasting. In
practice, data confusion is a significant issue as it can considerably impact
the effectiveness and accuracy of data analysis, machine learning models and
decision-making processes. In general, previous studies did not consider the
variability at various levels of granularity, thus resulting in inadequate
information utilization, which further exacerbated the issue of data confusion.
This paper proposes an unsupervised framework to realize multi-granularity
representation learning for time series. Specifically, we employed a
cross-granularity transformer to develop an association between fine- and
coarse-grained representations. In addition, we introduced a retrieval task as
an unsupervised training task to learn the multi-granularity representation of
time series. Moreover, a novel loss function was designed to obtain the
comprehensive multi-granularity representation of the time series via
unsupervised learning. The experimental results revealed that the proposed
framework demonstrates significant advantages over alternative representation
learning models.

本文提出了一个无监督的框架，实现了时间序列的多粒度表征学习，通过跨粒度变换器将细粒度和粗粒度表征进行关联，引入检索任务作为无监督训练任务来学习时间序列的多粒度表征，并设计了一种新颖的损失函数来通过无监督学习获取综合的多粒度时间序列表征，实验证明该框架相比其他表征学习模型具有显著优势。