A review on time series data mining

Document Type

Journal Article

Publication Date

2011

Keywords

Time series data mining, Representation, Similarity measure, Segmentation, Visualization

DOI

10.1016/j.engappai.2010.09.007

Abstract

Time series is an important class of temporal data objects and it can be easily obtained from scientific and financial applications. A time series is a collection of observations made chronologically. The nature of time series data includes: large in data size, high dimensionality and necessary to update continuously. Moreover time series data, which is characterized by its numerical and continuous nature, is always considered as a whole instead of individual numerical field. The increasing use of time series data has initiated a great deal of research and development attempts in the field of data mining. The abundant research on time series data mining in the last decade could hamper the entry of interested researchers, due to its complexity. In this paper, a comprehensive revision on the existing time series data mining research is given. They are generally categorized into representation and indexing, similarity measure, segmentation, visualization and mining. Moreover state-of-the-art research issues are also highlighted. The primary objective of this paper is to serve as a glossary for interested researchers to have an overall picture on the current time series data mining development and identify their potential research direction to further investigation.

Source Publication

Engineering Applications of Artificial Intelligence

Volume Number

24

Issue Number

1

ISSN

0952-1976

First Page

164

Last Page

181

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