Cross-domain Time Series Forecasting with Attention Sharing

Recent years have witnessed deep neural net-works gaining increasing popularity in the field oftime series forecasting. A primary reason of theirsuccess is their ability to effectively capture com-plex temporal dynamics across multiple relatedtime series. However, the advantages of thesedeep forecasters only start to emerge in the pres-ence of a sufficient amount of data. This poses achallenge for typical forecasting problems in prac-tice, where one either has a small number of timeseries, or limited observations per time series, orboth. To cope with the issue of data scarcity, wepropose a novel domain adaptation framework,Domain Adaptation Forecaster (DAF), that lever-ages the statistical strengths from another relevantdomain with abundant data samples (source) toimprove the performance on the domain of inter-est with limited data (target). In particular, we pro-pose an attention-based shared module with a do-main discriminator across domains as well as pri-vate modules for individual domains. This allowsus to jointly train the source and target domains bygenerating domain-invariant latent features whileretraining domain-specific features. Extensive ex-periments on various domains demonstrate thatour proposed method outperforms state-of-the-artbaselines on synthetic and real-world datasets.