Implicit neural representations (INRs) have recently emerged as a powerful
tool that provides an accurate and resolution-independent encoding of data.
Their robustness as general approximators has been shown in a wide variety of
data sources, with applications on image, sound, and 3D scene representation.
However, little attention has been given to leveraging these architectures for
the representation and analysis of time series data. In this paper, we analyze
the representation of time series using INRs, comparing different activation
functions in terms of reconstruction accuracy and training convergence speed.
We show how these networks can be leveraged for the imputation of time series,
with applications on both univariate and multivariate data. Finally, we propose
a hypernetwork architecture that leverages INRs to learn a compressed latent
representation of an entire time series dataset. We introduce an FFT-based loss
to guide training so that all frequencies are preserved in the time series. We
show that this network can be used to encode time series as INRs, and their
embeddings can be interpolated to generate new time series from existing ones.
We evaluate our generative method by using it for data augmentation, and show
that it is competitive against current state-of-the-art approaches for
augmentation of time series.

本文利用隐式神经表示技术分析了时间序列数据的表示问题，并提出了一种基于傅里叶变换的损失函数来指导网络训练的超网络结构，用于生成时间序列数据。我们证明了这个方法可以用于时间序列数据的扩充与生成，并且在此领域达到了最先进的方法的水平。