Multi-Granularity Framework for Unsupervised Representation Learning of
  Time Series
Chengyang Ye Qiang Ma
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.