Mining Frequent Distributions in Time Series

José Carlos Coutinho; João Mendes Moreira; Cláudio Rebelo de Sá

doi:10.1007/978-3-030-33617-2_28

Mining Frequent Distributions in Time Series

José Carlos Coutinho^*, João Mendes Moreira, Cláudio Rebelo de Sá

^*Corresponding author for this work

Datamanagement & Biometrics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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Abstract

Time series data is composed of observations of one or more variables along a time period. By analyzing the variability of the variables we can reveal patterns that repeat or that are correlated, which helps to understand the behaviour of the variables over time. Our method finds frequent distributions of a target variable in time series data and discovers relationships between frequent distributions in consecutive time intervals. The frequent distributions are found using a new method, and relationships between them are found using association rules mining.

Original language	English
Title of host publication	Intelligent Data Engineering and Automated Learning – IDEAL 2019
Subtitle of host publication	20th International Conference, Manchester, UK, November 14–16, 2019, Proceedings
Editors	Hujun Yin, Richard Allmendinger, David Camacho, Peter Tino, Antonio J. Tallón-Ballesteros, Ronaldo Menezes
Place of Publication	Cham
Publisher	Springer
Pages	271-279
Number of pages	9
Volume	II
ISBN (Electronic)	978-3-030-33617-2
ISBN (Print)	978-3-030-33616-5
DOIs	https://doi.org/10.1007/978-3-030-33617-2_28
Publication status	Published - 1 Jan 2019
Event	20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019 - University of Manchester, Manchester, United Kingdom Duration: 14 Nov 2019 → 16 Nov 2019 Conference number: 20 http://www.confercare.manchester.ac.uk/events/ideal2019/

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	11872
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Name	Information Systems and Applications, incl. Internet/Web, and HCI
Publisher	Springer

Conference

Conference	20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019
Abbreviated title	IDEAL 2019
Country/Territory	United Kingdom
City	Manchester
Period	14/11/19 → 16/11/19
Internet address	http://www.confercare.manchester.ac.uk/events/ideal2019/

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10.1007/978-3-030-33617-2_28

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Coutinho, J. C., Moreira, J. M., & de Sá, C. R. (2019). Mining Frequent Distributions in Time Series. In H. Yin, R. Allmendinger, D. Camacho, P. Tino, A. J. Tallón-Ballesteros, & R. Menezes (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2019: 20th International Conference, Manchester, UK, November 14–16, 2019, Proceedings (Vol. II, pp. 271-279). (Lecture Notes in Computer Science; Vol. 11872), (Information Systems and Applications, incl. Internet/Web, and HCI). Springer. https://doi.org/10.1007/978-3-030-33617-2_28

Coutinho, José Carlos ; Moreira, João Mendes ; de Sá, Cláudio Rebelo. / Mining Frequent Distributions in Time Series. Intelligent Data Engineering and Automated Learning – IDEAL 2019: 20th International Conference, Manchester, UK, November 14–16, 2019, Proceedings. editor / Hujun Yin ; Richard Allmendinger ; David Camacho ; Peter Tino ; Antonio J. Tallón-Ballesteros ; Ronaldo Menezes. Vol. II Cham : Springer, 2019. pp. 271-279 (Lecture Notes in Computer Science). (Information Systems and Applications, incl. Internet/Web, and HCI).

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booktitle = "Intelligent Data Engineering and Automated Learning – IDEAL 2019",

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note = "20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019, IDEAL 2019 ; Conference date: 14-11-2019 Through 16-11-2019",

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Coutinho, JC, Moreira, JM & de Sá, CR 2019, Mining Frequent Distributions in Time Series. in H Yin, R Allmendinger, D Camacho, P Tino, AJ Tallón-Ballesteros & R Menezes (eds), Intelligent Data Engineering and Automated Learning – IDEAL 2019: 20th International Conference, Manchester, UK, November 14–16, 2019, Proceedings. vol. II, Lecture Notes in Computer Science, vol. 11872, Information Systems and Applications, incl. Internet/Web, and HCI, Springer, Cham, pp. 271-279, 20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019, Manchester, United Kingdom, 14/11/19. https://doi.org/10.1007/978-3-030-33617-2_28

Mining Frequent Distributions in Time Series. / Coutinho, José Carlos; Moreira, João Mendes; de Sá, Cláudio Rebelo.
Intelligent Data Engineering and Automated Learning – IDEAL 2019: 20th International Conference, Manchester, UK, November 14–16, 2019, Proceedings. ed. / Hujun Yin; Richard Allmendinger; David Camacho; Peter Tino; Antonio J. Tallón-Ballesteros; Ronaldo Menezes. Vol. II Cham: Springer, 2019. p. 271-279 (Lecture Notes in Computer Science; Vol. 11872), (Information Systems and Applications, incl. Internet/Web, and HCI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Coutinho, José Carlos

AU - Moreira, João Mendes

AU - de Sá, Cláudio Rebelo

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N2 - Time series data is composed of observations of one or more variables along a time period. By analyzing the variability of the variables we can reveal patterns that repeat or that are correlated, which helps to understand the behaviour of the variables over time. Our method finds frequent distributions of a target variable in time series data and discovers relationships between frequent distributions in consecutive time intervals. The frequent distributions are found using a new method, and relationships between them are found using association rules mining.

AB - Time series data is composed of observations of one or more variables along a time period. By analyzing the variability of the variables we can reveal patterns that repeat or that are correlated, which helps to understand the behaviour of the variables over time. Our method finds frequent distributions of a target variable in time series data and discovers relationships between frequent distributions in consecutive time intervals. The frequent distributions are found using a new method, and relationships between them are found using association rules mining.

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BT - Intelligent Data Engineering and Automated Learning – IDEAL 2019

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A2 - Camacho, David

A2 - Tino, Peter

A2 - Tallón-Ballesteros, Antonio J.

A2 - Menezes, Ronaldo

PB - Springer

CY - Cham

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Coutinho JC, Moreira JM, de Sá CR. Mining Frequent Distributions in Time Series. In Yin H, Allmendinger R, Camacho D, Tino P, Tallón-Ballesteros AJ, Menezes R, editors, Intelligent Data Engineering and Automated Learning – IDEAL 2019: 20th International Conference, Manchester, UK, November 14–16, 2019, Proceedings. Vol. II. Cham: Springer. 2019. p. 271-279. (Lecture Notes in Computer Science). (Information Systems and Applications, incl. Internet/Web, and HCI). doi: 10.1007/978-3-030-33617-2_28

Mining Frequent Distributions in Time Series

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