  Capsule networks excel in understanding spatial relationships in 2D data for
vision related tasks. Even though they are not designed to capture 1D temporal
relationships, with TimeCaps we demonstrate that given the ability, capsule
networks excel in understanding temporal relationships. To this end, we
generate capsules along the temporal and channel dimensions creating two
temporal feature detectors which learn contrasting relationships. TimeCaps
surpasses the state-of-the-art results by achieving 96.21% accuracy on
identifying 13 Electrocardiogram (ECG) signal beat categories, while achieving
on-par results on identifying 30 classes of short audio commands. Further, the
instantiation parameters inherently learnt by the capsule networks allow us to
completely parameterize 1D signals which opens various possibilities in signal
processing.
