  Time series classification (TSC) is a challenging task that attracted many
researchers in the last few years. One main challenge in TSC is the diversity
of domains where time series data come from. Thus, there is no "one model that
fits all" in TSC. Some algorithms are very accurate in classifying a specific
type of time series when the whole series is considered, while some only target
the existence/non-existence of specific patterns/shapelets. Yet other
techniques focus on the frequency of occurrences of discriminating
patterns/features. This paper presents a new classification technique that
addresses the inherent diversity problem in TSC using a nature-inspired method.
The technique is stimulated by how flies look at the world through "compound
eyes" that are made up of thousands of lenses, called ommatidia. Each
ommatidium is an eye with its own lens, and thousands of them together create a
broad field of vision. The developed technique similarly uses different lenses
and representations to look at the time series, and then combines them for
broader visibility. These lenses have been created through
hyper-parameterisation of symbolic representations (Piecewise Aggregate and
Fourier approximations). The algorithm builds a random forest for each lens,
then performs soft dynamic voting for classifying new instances using the most
confident eyes, i.e, forests. We evaluate the new technique, coined Co-eye,
using the recently released extended version of UCR archive, containing more
than 100 datasets across a wide range of domains. The results show the benefits
of bringing together different perspectives reflecting on the accuracy and
robustness of Co-eye in comparison to other state-of-the-art techniques.
