Time-series data is generated ubiquitously from Internet-of-Things (IoT) infrastructure, connected and wearable devices, remote sensing, autonomous driving research and, audio-video communications, in enormous volumes. This paper investigates the potential of unsupervised representation learning for these time-series. In this paper, we use a novel data transformation along with novel unsupervised learning regime to transfer the learning from other domains to time-series where the former have extensive models heavily trained on very large labelled datasets. We conduct extensive experiments to demonstrate the potential of the proposed approach through time-series clustering.
翻译:时间序列数据来自互联网(IoT)基础设施、连通和可磨损设备、遥感、自主驱动研究和音像通信等大量数据,本文调查了这些时间序列无监督的代表性学习的潜力。在本文中,我们使用新的数据转换以及新的、不受监督的学习制度,将学习从其他领域转移到时间序列,前者在非常庞大的标签数据集方面受过大量培训的模型。我们进行了广泛的实验,通过时间序列集群展示拟议方法的潜力。