Grasp heuristic for time series compression with piecewise aggregate approximation - Archive ouverte HAL Access content directly
Journal Articles RAIRO - Operations Research Year : 2019

Grasp heuristic for time series compression with piecewise aggregate approximation

(1) , (1) , (1)
1

Abstract

The Piecewise Aggregate Approximation (PAA) is widely used in time series data mining because it allows to discretize, to reduce the length of time series and it is used as a subroutine by algorithms for patterns discovery, indexing, and classification of time series. However, it requires setting one parameter: the number of segments to consider during the discretization. The optimal parameter value is highly data dependent in particular on large time series. This paper presents a heuristic for time series compression with PAA which minimizes the loss of information. The heuristic is built upon the well known metaheuristic GRASP and strengthened with an inclusion of specific global search component. An extensive experimental evaluation on several time series datasets demonstrated its efficiency and effectiveness in terms of compression ratio, compression interpretability and classification.
Fichier principal
Vignette du fichier
2019_RAIRO_ro170169_published.pdf (4.1 Mo) Télécharger le fichier
Origin : Publisher files allowed on an open archive
Loading...

Dates and versions

hal-02058154 , version 1 (05-03-2019)

Identifiers

Cite

Vanel Steve Siyou Fotso, Mephu Nguifo Engelbert, Philippe Vaslin. Grasp heuristic for time series compression with piecewise aggregate approximation. RAIRO - Operations Research, 2019, 53 (1), pp.243-259. ⟨10.1051/ro/2018089⟩. ⟨hal-02058154⟩
99 View
131 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More