Variable size segmentation for efficient representation and querying of non-uniform time series datasets - Archive ouverte HAL Access content directly
Conference Papers Year : 2022

Variable size segmentation for efficient representation and querying of non-uniform time series datasets

(1) , (1) , (1)
1
Lamia Djebour
  • Function : Author
  • PersonId : 1119503
Reza Akbarinia
Florent Masseglia

Abstract

Existing approaches for time series similarity computing are the core of many data analytics tasks. Given the considered data volumes, or simply the need for fast response times, they often rely on shorter representations, usually with information loss. This incurs approximate comparisons where precision is a major issue. We present and experimentally evaluate ASAX, a new approach for segmenting time series before their transformation into symbolic representations. ASAX reduces significantly the information loss incurred by possible splittings at different steps of the representation calculation, particularly for datasets with unbalanced (nonuniform) distributions. We provide theoretical guarantees on the lower bound of similarity measures, and our experiments illustrate that our method outperforms the state of the art, with significant gain in precision for datasets with unbalanced distributions.
Fichier principal
Vignette du fichier
Entropy-SAC.pdf (3.9 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

lirmm-03806053 , version 1 (07-10-2022)

Identifiers

Cite

Lamia Djebour, Reza Akbarinia, Florent Masseglia. Variable size segmentation for efficient representation and querying of non-uniform time series datasets. SAC 2022 - 37th ACM/SIGAPP Symposium on Applied Computing, Apr 2022, Virtual Event, United States. pp.395-402, ⟨10.1145/3477314.3507000⟩. ⟨lirmm-03806053⟩
24 View
2 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More