Optimized word-size time series representation method using a genetic algorithm with a flexible encoding scheme

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

Abstract

Performing time series mining tasks directly on raw data is inefficient, therefore these data require representation methods that transform them into low-dimension spaces where they can be managed more efficiently. Owing to its simplicity, the piecewise aggregate approximation is a popular time series representation method. But this method uses a uniform word-size for all the segments in the time series, which reduces the quality of the representation. Although some alternatives use representations with different word-sizes in a way that reflects the various information contents of different segments, such methods apply a complicated representation scheme, as it uses a different representation for each time series in the dataset. In this paper we present two modifications of the original piecewise aggregate approximation. The novelty of these modifications is that they use different word-sizes, which allows for a flexible representation that reflects the level of activity in each segment, yet these new medications address this problem on a dataset-level, which simplifies establishing a lower bounding distance. The word-sizes are determined through an optimization process. The experiments we conducted on a variety of time series datasets validate the two new modifications.

Original languageEnglish
Title of host publicationAIIA 2016
Subtitle of host publicationAdvances in Artificial Intelligence - 15th International Conference of the Italian Association for Artificial Intelligence, Proceedings
EditorsGiovanni Adorni, Marco Maratea, Stefano Cagnoni, Marco Gori
PublisherSpringer-Verlag Italia
Pages26-34
Number of pages9
ISBN (Print)9783319491295
DOIs
Publication statusPublished - 5 Nov 2016
Externally publishedYes
Event15th International Conference on Italian Association for Artificial Intelligence, AIIA 2016 - Genova, Italy
Duration: 28 Nov 20161 Dec 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10037 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th International Conference on Italian Association for Artificial Intelligence, AIIA 2016
CountryItaly
CityGenova
Period28/11/161/12/16

Keywords

  • Genetic algorithm
  • Time series representation
  • Word-size

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Muhammad Fuad, M. M. (2016). Optimized word-size time series representation method using a genetic algorithm with a flexible encoding scheme. In G. Adorni, M. Maratea, S. Cagnoni, & M. Gori (Eds.), AIIA 2016: Advances in Artificial Intelligence - 15th International Conference of the Italian Association for Artificial Intelligence, Proceedings (pp. 26-34). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10037 LNAI). Springer-Verlag Italia. https://doi.org/10.1007/978-3-319-49130-1_3