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

TimeCaps: Capturing Time Series Data With Capsule Networks