  Due to effective pattern mining and feature representation, neural
forecasting models based on deep learning have achieved great progress. The
premise of effective learning is to collect sufficient data. However, in time
series forecasting, it is difficult to obtain enough data, which limits the
performance of neural forecasting models. To alleviate the data scarcity
limitation, we design Sequence Decomposition Adaptation Network (SeDAN) which
is a novel transfer architecture to improve forecasting performance on the
target domain by aligning transferable knowledge from cross-domain datasets.
Rethinking the transferability of features in time series data, we propose
Implicit Contrastive Decomposition to decompose the original features into
components including seasonal and trend features, which are easier to transfer.
Then we design the corresponding adaptation methods for decomposed features in
different domains. Specifically, for seasonal features, we perform joint
distribution adaptation and for trend features, we design an Optimal Local
Adaptation. We conduct extensive experiments on five benchmark datasets for
multivariate time series forecasting. The results demonstrate the effectiveness
of our SeDAN. It can provide more efficient and stable knowledge transfer.
