Identifying Stochasticity in Time-Series with Autoencoder-Based Content-aware 2D Representation: Application to Black Hole Data
This work addresses the challenge of identifying stochasticity in time-series for astronomical data analysis, but it appears incremental as it builds on existing autoencoder and classification methods.
The authors tackled the problem of classifying time-series as stochastic or non-stochastic to understand physical processes, using an autoencoder-based 2D representation method, and achieved results with concurrence in labels across 12 classes of black hole data compared to existing techniques.
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.