  Recently, deep neural networks have gained increasing popularity in the field
of time series forecasting. A primary reason for their success is their ability
to effectively capture complex temporal dynamics across multiple related time
series. The advantages of these deep forecasters only start to emerge in the
presence of a sufficient amount of data. This poses a challenge for typical
forecasting problems in practice, where there is a limited number of time
series or observations per time series, or both. To cope with this data
scarcity issue, we propose a novel domain adaptation framework, Domain
Adaptation Forecaster (DAF). DAF leverages statistical strengths from a
relevant domain with abundant data samples (source) to improve the performance
on the domain of interest with limited data (target). In particular, we use an
attention-based shared module with a domain discriminator across domains and
private modules for individual domains. We induce domain-invariant latent
features (queries and keys) and retrain domain-specific features (values)
simultaneously to enable joint training of forecasters on source and target
domains. A main insight is that our design of aligning keys allows the target
domain to leverage source time series even with different characteristics.
Extensive experiments on various domains demonstrate that our proposed method
outperforms state-of-the-art baselines on synthetic and real-world datasets,
and ablation studies verify the effectiveness of our design choices.

Machine Learning;Machine Learning