2023
Measuring Deviation from Stochasticity in Time-Series Using Autoencoder Based Time-Invariant Representation: Application to Black Hole Data
ICASSP 2023accepted
We propose a novel approach to quantify "deviation from stochasticity" (DS) in a time-series. This is important to determine if the time-series is coming from a physical phenomenon or if it is noise. This approach utilizes time-invariant representation obtained using time- and frequency-domain analy…