  Domain adaptation on time-series data is often encountered in the industry
but received limited attention in academia. Most of the existing domain
adaptation methods for time-series data borrow the ideas from the existing
methods for non-time series data to extract the domain-invariant
representation. However, two peculiar difficulties to time-series data have not
been solved. 1) It is not a trivial task to model the domain-invariant and
complex dependence among different timestamps. 2) The domain-variant
information is important but how to leverage them is almost underexploited.
Fortunately, the stableness of causal structures among different domains
inspires us to explore the structures behind the time-series data. Based on
this inspiration, we investigate the domain-invariant unweighted sparse
associative structures and the domain-variant strengths of the structures. To
achieve this, we propose Sparse Associative structure alignment by learning
Invariance and Variance (SASA-IV in short), a model that simultaneously aligns
the invariant unweighted spare associative structures and considers the variant
information for time-series unsupervised domain adaptation. Technologically, we
extract the domain-invariant unweighted sparse associative structures with a
unidirectional alignment restriction and embed the domain-variant strengths via
a well-designed autoregressive module. Experimental results not only testify
that our model yields state-of-the-art performance on three real-world datasets
but also provide some insightful discoveries on knowledge transfer.
