RT Journal Article
T1 Exploring multiprocessor approaches to time series analysis
A1 Quislant-del-Barrio, Ricardo
A1 Gutiérrez-Carrasco, Eladio Damián
A1 Plata-González, Óscar Guillermo
K1 Arquitectura de ordenadores
K1 Análisis de series temporales
K1 Ordenadores - Memorias
AB A time series is a chronologically ordered set of samples of a real-valued variable that can have millions of observations. Time series analysis seeks extracting models in a large variety of domains [31] such as epidemiology, DNA analysis, economics, geophysics, speech recognition, etc. Particularly, motif [4] (similarity) and discord [13] (anomaly) discovery has become one of the most frequently used primitives in time series data mining [20], [2], [32], [7], [34], [1]. It poses the problem of solving the all-pairs-similarity-search (also known as similarity join). Specifically, given a time series broken down into subsequences, retrieve the most similar subsequences (motifs) and the most different ones (discords).One of the state-of-the-art methods for motif and discord discovery is Matrix Profile [35]. It solves the similarity join problem and allows time-manageable computation of very large time series. In this work, we focus on this technique, which features the possibility of detecting similarities, anomalies, and predicting outcomes. It provides full joins without the need for specifying a similarity threshold, which is a very challenging task in this domain. The matrix profile is another time series representing the minimum distance subsequence for each subsequence in the time series (motifs). Maximum distance values of the profile highlight the most dissimilar subsequences (discords).
PB Elsevier
YR 2024
FD 2024-02-08
LK https://hdl.handle.net/10630/30443
UL https://hdl.handle.net/10630/30443
LA eng
NO Ricardo Quislant, Eladio Gutierrez, Oscar Plata, Exploring multiprocessor approaches to time series analysis, Journal of Parallel and Distributed Computing, Volume 188, 2024, 104855, ISSN 0743-7315, https://doi.org/10.1016/j.jpdc.2024.104855
NO Funding for open Access charge: Universidad de Málaga / CBUA.This work has been supported by the Spanish Government under projects PID2019-105396RB-I00 and PID2022-136575OB-I00.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 25 feb 2026