A survey on time series data mining

Amin Fakhrazari; Hamid Vakilzadian

doi:10.1109/EIT.2017.8053409

A survey on time series data mining

Amin Fakhrazari, Hamid Vakilzadian

Electrical & Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

24 Scopus citations

Abstract

In this paper, an overview on existing data mining techniques for time series modeling and analysis will be provided. Classification of available literature on time series data mining shows that the main research orientations can be divided into three subfields: Dimensionality Reduction (Time Series Representation), Similarity Measures and Data Mining Tasks.

Original language	English (US)
Title of host publication	2017 IEEE International Conference on Electro Information Technology, EIT 2017
Publisher	IEEE Computer Society
Pages	476-481
Number of pages	6
ISBN (Electronic)	9781509047673
DOIs	https://doi.org/10.1109/EIT.2017.8053409
State	Published - Sep 27 2017
Event	2017 IEEE International Conference on Electro Information Technology, EIT 2017 - Lincoln, United States Duration: May 14 2017 → May 17 2017

Publication series

Name	IEEE International Conference on Electro Information Technology
ISSN (Print)	2154-0357
ISSN (Electronic)	2154-0373

Other

Other	2017 IEEE International Conference on Electro Information Technology, EIT 2017
Country/Territory	United States
City	Lincoln
Period	5/14/17 → 5/17/17

Keywords

Time series analysis
artificial intelligence
data mining
machine learning

ASJC Scopus subject areas

Computer Science Applications
Information Systems
Control and Systems Engineering
Electrical and Electronic Engineering

Access to Document

10.1109/EIT.2017.8053409

Cite this

Fakhrazari, A & Vakilzadian, H 2017, A survey on time series data mining. in 2017 IEEE International Conference on Electro Information Technology, EIT 2017., 8053409, IEEE International Conference on Electro Information Technology, IEEE Computer Society, pp. 476-481, 2017 IEEE International Conference on Electro Information Technology, EIT 2017, Lincoln, United States, 5/14/17. https://doi.org/10.1109/EIT.2017.8053409

@inproceedings{aff6231758714d088647dbae2b601451,

title = "A survey on time series data mining",

abstract = "In this paper, an overview on existing data mining techniques for time series modeling and analysis will be provided. Classification of available literature on time series data mining shows that the main research orientations can be divided into three subfields: Dimensionality Reduction (Time Series Representation), Similarity Measures and Data Mining Tasks.",

keywords = "Time series analysis, artificial intelligence, data mining, machine learning",

author = "Amin Fakhrazari and Hamid Vakilzadian",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Electro Information Technology, EIT 2017 ; Conference date: 14-05-2017 Through 17-05-2017",

year = "2017",

month = sep,

day = "27",

doi = "10.1109/EIT.2017.8053409",

language = "English (US)",

series = "IEEE International Conference on Electro Information Technology",

publisher = "IEEE Computer Society",

pages = "476--481",

booktitle = "2017 IEEE International Conference on Electro Information Technology, EIT 2017",

}

TY - GEN

T1 - A survey on time series data mining

AU - Fakhrazari, Amin

AU - Vakilzadian, Hamid

PY - 2017/9/27

Y1 - 2017/9/27

N2 - In this paper, an overview on existing data mining techniques for time series modeling and analysis will be provided. Classification of available literature on time series data mining shows that the main research orientations can be divided into three subfields: Dimensionality Reduction (Time Series Representation), Similarity Measures and Data Mining Tasks.

AB - In this paper, an overview on existing data mining techniques for time series modeling and analysis will be provided. Classification of available literature on time series data mining shows that the main research orientations can be divided into three subfields: Dimensionality Reduction (Time Series Representation), Similarity Measures and Data Mining Tasks.

KW - Time series analysis

KW - artificial intelligence

KW - data mining

KW - machine learning

UR - http://www.scopus.com/inward/record.url?scp=85033666594&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85033666594&partnerID=8YFLogxK

U2 - 10.1109/EIT.2017.8053409

DO - 10.1109/EIT.2017.8053409

M3 - Conference contribution

AN - SCOPUS:85033666594

T3 - IEEE International Conference on Electro Information Technology

SP - 476

EP - 481

BT - 2017 IEEE International Conference on Electro Information Technology, EIT 2017

PB - IEEE Computer Society

T2 - 2017 IEEE International Conference on Electro Information Technology, EIT 2017

Y2 - 14 May 2017 through 17 May 2017

ER -

A survey on time series data mining

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this