Modeling time series data remains a pervasive issue as the temporal dimension
is inherent to numerous domains. Despite significant strides in time series
forecasting, high noise-to-signal ratio, non-normality, non-stationarity, and
lack of data continue challenging practitioners. In response, we leverage a
simple representation augmentation technique to overcome these challenges. Our
augmented representation acts as a statistical-space prior encoded at each time
step. In response, we name our method Statistical-space Augmented
Representation (SSAR). The underlying high-dimensional data-generating process
inspires our representation augmentation. We rigorously examine the empirical
generalization performance on two data sets with two downstream temporal
learning algorithms. Our approach significantly beats all five up-to-date
baselines. Moreover, the highly modular nature of our approach can easily be
applied to various settings. Lastly, fully-fledged theoretical perspectives are
available throughout the writing for a clear and rigorous understanding.

通过表示增强技术，我们的方法在时间序列数据建模方面取得显著成效，并在实证泛化性能方面明显优于最新的五个基准模型，可广泛应用于各种领域。