The presence of smart objects is increasingly widespread and their ecosystem, also known as Internet of Things, is relevant in many different application scenarios. The huge amount of temporally annotated data produced by these smart devices demand for efficient techniques for transfer and storage of time series data. Compression techniques play an important role toward this goal and, despite the fact that standard compression methods could be used with some benefit, there exist several ones that specifically address the case of time series by exploiting their peculiarities to achieve a more effective compression and a more accurate decompression in the case of lossy compression techniques. This paper provides a state-of-the-art survey of the principal time series compression techniques, proposing a taxonomy to classify them considering their overall approach and their characteristics. Furthermore, we analyze the performances of the selected algorithms by discussing and comparing the experimental results that where provided in the original articles. The goal of this paper is to provide a comprehensive and homogeneous reconstruction of the state-of-the-art which is currently fragmented across many papers that use different notations and where the proposed methods are not organized according to a classification.

翻译：智能物体的存在日益广泛,其生态系统,又称物联网,在许多不同的应用情景中都具有相关性。这些智能装置对转让和储存时间序列数据的有效技术提出了大量时间上附加说明的数据,这些智能装置对转让和储存时间序列数据提出了大量时间上附加说明的数据。压缩技术对实现这一目标起着重要作用。尽管标准压缩方法的使用可以带来一些好处,但有一些压缩技术具体涉及时间序列的情况,利用这些技术的独特性实现更加有效的压缩,并在丢失压缩技术的情况下实现更准确的减压。本文对主要的时间序列压缩技术进行了最新的最新调查,建议进行分类,以考虑到这些技术的总体方法和特点。此外,我们通过讨论和比较原始文章中提供的实验结果,分析选定算法的绩效。本文的目的是对目前分散于使用不同符号的许多论文以及没有按照分类安排拟议方法的地方,全面、统一地重建最新工艺。