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.
翻译:最近,深心神经网络在时间序列预测领域越来越受欢迎,其成功的主要原因是它们能够有效捕捉多个相关时间序列的复杂时间动态。这些深心预报器的优势只是在有足够数据的情况下才开始出现。这对典型的实际预测问题提出了挑战,因为每个时间序列或两者都有数量有限的时间序列或观测。为了应对这一数据稀缺问题,我们提议了一个全新的领域适应框架,即Dome适应预报器(DAF)。DAF利用一个相关领域的统计优势,利用大量数据样本(来源)改进感兴趣的领域的绩效(目标),用有限的数据(目标)改进领域的业绩。特别是,我们使用一个基于关注的共享模块,与一个域区分器和单个领域的私人模块共享。我们引入了域差异性潜伏特征(询问和钥匙)和再定位域特性(价值),以便联合培训源和目标领域的预报员。一个主要的见解是,我们对钥匙的设计使得目标领域能够利用源时间序列,即使具有不同的特性(目标)。特别是,我们使用一个基于区域域域域域域的域区分共享共享共享的共享模块,并用我们拟议的方法校验了我们的拟议方法。