Mining Frequent Distributions in Time Series

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review


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 languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning – IDEAL 2019
Subtitle of host publication20th International Conference, Manchester, UK, November 14–16, 2019, Proceedings
EditorsHujun Yin, Richard Allmendinger, David Camacho, Peter Tino, Antonio J. Tallón-Ballesteros, Ronaldo Menezes
Place of PublicationCham
Number of pages9
ISBN (Electronic)978-3-030-33617-2
ISBN (Print)978-3-030-33616-5
Publication statusPublished - 1 Jan 2019
Event20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019 - University of Manchester, Manchester, United Kingdom
Duration: 14 Nov 201916 Nov 2019
Conference number: 20

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameInformation Systems and Applications, incl. Internet/Web, and HCI


Conference20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019
Abbreviated titleIDEAL 2019
CountryUnited Kingdom
Internet address

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