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One main challenge in TSC is the diversity of domains where time series data come from. Thus, there is no \u201cone model that fits all\u201d 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 \u201ccompound eyes\u201d 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.<\/jats:p>","DOI":"10.1007\/s10994-020-05887-3","type":"journal-article","created":{"date-parts":[[2020,8,26]],"date-time":"2020-08-26T20:02:46Z","timestamp":1598472166000},"page":"2029-2061","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Co-eye: a multi-resolution ensemble classifier for symbolically approximated time series"],"prefix":"10.1007","volume":"109","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1291-2918","authenticated-orcid":false,"given":"Zahraa S.","family":"Abdallah","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed Medhat","family":"Gaber","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,8,26]]},"reference":[{"key":"5887_CR1","unstructured":"Bagnall, A.,\u00a0Lines, J., Vickers, W. 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