  There is nowadays a constant flux of data being generated and collected in
all types of real world systems. These data sets are often indexed by time,
space or both requiring appropriate approaches to analyze the data. In
univariate settings, time series analysis is a mature and solid field. However,
in multivariate contexts, time series analysis still presents many limitations.
In order to address these issues, the last decade has brought approaches based
on network science. These methods involve transforming an initial time series
data set into one or more networks, which can be analyzed in depth to provide
insight into the original time series. This review provides a comprehensive
overview of existing mapping methods for transforming time series into networks
for a wide audience of researchers and practitioners in machine learning, data
mining and time series. Our main contribution is a structured review of
existing methodologies, identifying their main characteristics and their
differences. We describe the main conceptual approaches, provide authoritative
references and give insight into their advantages and limitations in a unified
notation and language. We first describe the case of univariate time series,
which can be mapped to single layer networks, and we divide the current
mappings based on the underlying concept: visibility, transition and proximity.
We then proceed with multivariate time series discussing both single layer and
multiple layer approaches. Although still very recent, this research area has
much potential and with this survey we intend to pave the way for future
research on the topic.
