In this work, we report an autoencoder-based 2D representation to classify a
time-series as stochastic or non-stochastic, to understand the underlying
physical process. Content-aware conversion of 1D time-series to 2D
representation, that simultaneously utilizes time- and frequency-domain
characteristics, is proposed. An autoencoder is trained with a loss function to
learn latent space (using both time- and frequency domains) representation,
that is designed to be, time-invariant. Every element of the time-series is
represented as a tuple with two components, one each, from latent space
representation in time- and frequency-domains, forming a binary image. In this
binary image, those tuples that represent the points in the time-series,
together form the ``Latent Space Signature" (LSS) of the input time-series. The
obtained binary LSS images are fed to a classification network. The
EfficientNetv2-S classifier is trained using 421 synthetic time-series, with
fair representation from both categories. The proposed methodology is evaluated
on publicly available astronomical data which are 12 distinct temporal classes
of time-series pertaining to the black hole GRS 1915 + 105, obtained from RXTE
satellite. Results obtained using the proposed methodology are compared with
existing techniques. Concurrence in labels obtained across the classes,
illustrates the efficacy of the proposed 2D representation using the latent
space co-ordinates. The proposed methodology also outputs the confidence in the
classification label.

本研究提出一种基于自编码器的 2D 表示法，用于对时间序列进行分类，从而理解底层物理过程。使用联合时间和频率维度特征的内容感知的 1D 时间序列到 2D 表示的转换被提出。使用 421 个合成时间序列训练了 EfficientNetv2-S 分类器，并在公开的天文数据集上进行了评估，证明了该方法的有效性。