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。

通过增强表示编码时间统计空间先验

Encoding Temporal Statistical-space Priors via Augmented Representation

Interpretable representation learning has been playing a key role in creative
intelligent systems. In the music domain, current learning algorithms can
successfully learn various features such as pitch, timbre, chord, texture, etc.
However, most methods rely heavily on music domain knowledge. It remains an
open question what general computational principles give rise to interpretable
representations, especially low-dim factors that agree with human perception.
In this study, we take inspiration from modern physics and use physical
symmetry as a self-consistency constraint for the latent space. Specifically,
it requires the prior model that characterises the dynamics of the latent
states to be equivariant with respect to certain group transformations. We show
that physical symmetry leads the model to learn a linear pitch factor from
unlabelled monophonic music audio in a self-supervised fashion. In addition,
the same methodology can be applied to computer vision, learning a 3D Cartesian
space from videos of a simple moving object without labels. Furthermore,
physical symmetry naturally leads to representation augmentation, a new
technique which improves sample efficiency.

本研究通过利用物理对称性作为潜空间的一致性约束，并将其应用于后续的无监督学习中，以学习诸如线性音高因素之类的低维数据表示，并进一步提出了用于改进样本利用率的表示增强技术。