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.

中文翻译：

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Co-eye：用于符号近似时间序列的多分辨率集成分类器

时间序列分类（TSC）是一项具有挑战性的任务，在过去几年中吸引了许多研究人员。TSC 的一项主要挑战是时间序列数据来自的域的多样性。因此，TSC 中不存在“适合所有人的模型”。当考虑整个序列时，某些算法在对特定类型的时间序列进行分类时非常准确，而有些算法仅针对特定模式/shapelets 的存在/不存在。还有其他技术专注于区分模式/特征的出现频率。本文提出了一种新的分类技术，该技术使用受自然启发的方法解决了 TSC 中固有的多样性问题。该技术受到苍蝇如何通过由数千个称为 ommatidia 的镜片组成的“复眼”观察世界的刺激。每个小眼都是一只眼睛，有自己的晶状体，成千上万的小眼共同创造出广阔的视野。开发的技术类似地使用不同的镜头和表示来查看时间序列，然后将它们组合起来以获得更广泛的可见性。这些镜头是通过符号表示（分段聚合和傅立叶近似）的超参数化创建的。该算法为每个镜头构建一个随机森林，然后执行软动态投票，使用最有信心的眼睛（即森林）对新实例进行分类。我们使用最近发布的 UCR 档案扩展版本评估了创造 Co-eye 的新技术，其中包含跨越广泛领域的 100 多个数据集。