  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.
