Visual analytics for time series data has received a considerable amount of attention.
Different approaches have been developed to understand the characteristics of the
data and obtain meaningful statistics in order to explore the underlying processes,
identify and estimate trends, make decisions and predict the future. The machine learning
and visualization areas share a focus on extracting information from data. In this
paper, we consider not only automatic methods but also interactive exploration. The
ability to embed efficient machine learning techniques (clustering and classification)
in interactive visualization systems is highly desirable in order to gain the most
from both humans and computers. We present a literature review of some of the most
important publications in the field and classify over 60 published papers from six
different perspectives. This review intends to clarify the major concepts with which
clustering or classification algorithms are used in visual analytics for time series
data and provide a valuable guide for both new researchers and experts in the emerging
field of integrating machine learning techniques into visual analytics.