Bag-of-temporal-SIFT-words for Time series classification

Adeline Bailly; Simon Malinowski; Romain Tavenard; Thomas Guyet; Lætitia Chapel

Bag-of-temporal-SIFT-words for Time series classification

Adeline Bailly
, Simon Malinowski
, Romain Tavenard
, Thomas Guyet
, Lætitia Chapel

IRISA
University of Rennes
IRDL

Research output: Contribution to journal › Conference article › peer-review

2 Citations (Scopus)

Abstract

Time series classification is an application of particular interest with the increase of data to monitor. Classical techniques for time series classification rely on point-to-point distances. Recently, Bag-of-Words approaches have been used in this context. Words are quantized versions of simple features extracted from sliding windows. The SIFT framework has proved efficient for image classification. In this paper, we design a time series classification scheme that builds on the SIFT framework adapted to time series to feed a Bag-of-Words. Experimental results show competitive performance with respect to classical techniques.

Original language	English
Pages (from-to)	11-17
Number of pages	7
Journal	CEUR Workshop Proceedings
Volume	1425
Publication status	Published - 1 Jan 2015
Externally published	Yes
Event	1st International Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2015 - Workshop co-located with The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2015 - Porto, Portugal Duration: 11 Sept 2015 → …

Keywords

Bag-of-Words
Botsw
SIFT
Time series classification

Cite this

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title = "Bag-of-temporal-SIFT-words for Time series classification",

abstract = "Time series classification is an application of particular interest with the increase of data to monitor. Classical techniques for time series classification rely on point-to-point distances. Recently, Bag-of-Words approaches have been used in this context. Words are quantized versions of simple features extracted from sliding windows. The SIFT framework has proved efficient for image classification. In this paper, we design a time series classification scheme that builds on the SIFT framework adapted to time series to feed a Bag-of-Words. Experimental results show competitive performance with respect to classical techniques.",

keywords = "Bag-of-Words, Botsw, SIFT, Time series classification",

author = "Adeline Bailly and Simon Malinowski and Romain Tavenard and Thomas Guyet and L{\ae}titia Chapel",

note = "Publisher Copyright: Copyright {\textcopyright} 2015 for This Paper by Its Authors.; 1st International Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2015 - Workshop co-located with The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2015 ; Conference date: 11-09-2015",

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T1 - Bag-of-temporal-SIFT-words for Time series classification

AU - Bailly, Adeline

AU - Malinowski, Simon

AU - Tavenard, Romain

AU - Guyet, Thomas

AU - Chapel, Lætitia

PY - 2015/1/1

Y1 - 2015/1/1

N2 - Time series classification is an application of particular interest with the increase of data to monitor. Classical techniques for time series classification rely on point-to-point distances. Recently, Bag-of-Words approaches have been used in this context. Words are quantized versions of simple features extracted from sliding windows. The SIFT framework has proved efficient for image classification. In this paper, we design a time series classification scheme that builds on the SIFT framework adapted to time series to feed a Bag-of-Words. Experimental results show competitive performance with respect to classical techniques.

AB - Time series classification is an application of particular interest with the increase of data to monitor. Classical techniques for time series classification rely on point-to-point distances. Recently, Bag-of-Words approaches have been used in this context. Words are quantized versions of simple features extracted from sliding windows. The SIFT framework has proved efficient for image classification. In this paper, we design a time series classification scheme that builds on the SIFT framework adapted to time series to feed a Bag-of-Words. Experimental results show competitive performance with respect to classical techniques.

KW - Bag-of-Words

KW - Botsw

KW - SIFT

KW - Time series classification

UR - https://www.scopus.com/pages/publications/84944264523

M3 - Conference article

AN - SCOPUS:84944264523

SN - 1613-0073

VL - 1425

SP - 11

EP - 17

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

T2 - 1st International Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2015 - Workshop co-located with The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2015

Y2 - 11 September 2015

ER -

Bag-of-temporal-SIFT-words for Time series classification

Abstract

Keywords

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