  The phenomenon of Gravitational Wave (GW) analysis has grown in popularity as
technology has advanced and the process of observing gravitational waves has
become more precise. Although the sensitivity and the frequency of observation
of GW signals are constantly improving, the possibility of noise in the
collected GW data remains. In this paper, we propose two new Machine and Deep
learning ensemble approaches (i.e., ShallowWaves and DeepWaves Ensembles) for
detecting different types of noise and patterns in datasets from GW
observatories. Our research also investigates various Machine and Deep Learning
techniques for multi-class classification and provides a comprehensive
benchmark, emphasizing the best results in terms of three commonly used
performance metrics (i.e., accuracy, precision, and recall). We train and test
our models on a dataset consisting of annotated time series from real-world
data collected by the Advanced Laser Interferometer GW Observatory (LIGO). We
empirically show that the best overall accuracy is obtained by the proposed
DeepWaves Ensemble, followed close by the ShallowWaves Ensemble.
