Download Recent Developments in Time Series Data Mining

Recent Advances in Time-Series Data Mining
Leonidas Karamitopoulos
ATEI of Thessaloniki
P.O. Box 141
GR-57400 Sindos
[email protected]
Georgios Evangelidis
University of Macedonia
156 Egnatia St.,
GR-54006, Thessaloniki
[email protected]
Abstract
In the last decade there has been an increasing interest in mining time-series data since huge
amounts of data are generated by several procedures in almost every domain (business,
industry, medicine, science, etc.). Moreover, considering image or video data as time-series
data, expands the list of time-series databases that need to be mined. During this period of
time, hundreds of papers have been published covering all aspects of time-series data mining,
namely, dimensionality reduction or representation techniques, indexing, clustering,
classification, novelty detection, motif discovery, etc. The objective of this paper is to serve
both as an overview of the most recent advances in the field of time-series data mining and as
a reference to applications on real world cases.
Although a general overview is included, the literature review is focused mainly on papers of
the last five years. The papers that are referenced in this paper have been selected in order to
satisfy certain criteria, such as, the quality of the conferences or journals they appeared in and
their popularity (except from the most recent papers). The survey is comprehensive regarding
conferences or journals related to the computing science community, but it was not intended
to be exhaustive since the emerging field of data mining has attracted the interest of
researchers from many other communities such as the medical or the meteorological.
Most of the contributions focus on proposing different dimensionality reduction approaches
and providing novel similarity measures in order to deal with the unique characteristics of
time-series data, specifically, the high dimensionality, the high feature correlation and the
large amounts of noise, and to improve the performance of the existing data mining
techniques. The motif discovery and novelty detection data mining tasks have gained an
increasing interest by the data mining community. Although there is a vast amount of research
in time-series data mining algorithms and methods, few real world applications appear in the
literature. The majority of the implementations are performed on synthetic datasets or on real
datasets which often are not large enough to justify their practical usefulness.
Keywords: time series, data mining