  Domain adaptation on time series data is an important but challenging task.
Most of the existing works in this area are based on the learning of the
domain-invariant representation of the data with the help of restrictions like
MMD. However, such extraction of the domain-invariant representation is a
non-trivial task for time series data, due to the complex dependence among the
timestamps. In detail, in the fully dependent time series, a small change of
the time lags or the offsets may lead to difficulty in the domain invariant
extraction. Fortunately, the stability of the causality inspired us to explore
the domain invariant structure of the data. To reduce the difficulty in the
discovery of causal structure, we relax it to the sparse associative structure
and propose a novel sparse associative structure alignment model for domain
adaptation. First, we generate the segment set to exclude the obstacle of
offsets. Second, the intra-variables and inter-variables sparse attention
mechanisms are devised to extract associative structure time-series data with
considering time lags. Finally, the associative structure alignment is used to
guide the transfer of knowledge from the source domain to the target one.
Experimental studies not only verify the good performance of our methods on
three real-world datasets but also provide some insightful discoveries on the
transferred knowledge.
