Treffer: Characterization of time series for analyzing of the evolution of time series clusters

Title:
Characterization of time series for analyzing of the evolution of time series clusters
Authors:
SERRA, Ana P, ZARATE, Luis E
Source:
Expert systems with applications. 42(1):596-611
Publisher Information:
Amsterdam: Elsevier, 2015.
Publication Year:
2015
Physical Description:
print, 1/4 p
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Probabilités et statistiques, Probability and statistics, Statistiques, Statistics, Inférence à partir de processus stochastiques; analyse des séries temporelles, Inference from stochastic processes; time series analysis, Sciences appliquees, Applied sciences, Recherche operationnelle. Gestion, Operational research. Management science, Recherche opérationnelle et modèles formalisés de gestion, Operational research and scientific management, Théorie de la décision. Théorie de l'utilité, Decision theory. Utility theory, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Logiciel, Software, Organisation des mémoires. Traitement des données, Memory organisation. Data processing, Traitement des données. Listes et chaînes de caractères, Data processing. List processing. Character string processing, Systèmes d'information. Bases de données, Information systems. Data bases, Amas, Cluster, Montón, Analyse amas, Cluster analysis, Analisis cluster, Analyse donnée, Data analysis, Análisis datos, Base de données, Database, Base dato, Classification hiérarchique, Hierarchical classification, Clasificación jerarquizada, Fouille donnée, Data mining, Busca dato, Lissage exponentiel, Exponential smoothing, Alisado exponencial, Modélisation, Modeling, Modelización, Méthodologie, Methodology, Metodología, Partition, Partición, Prise de décision, Decision making, Toma decision, Scénario, Script, Argumento, Série temporelle, Time series, Serie temporal, Temps occupation, Occupation time, Tiempo ocupación, Base donnée temporelle, Temporal databases, Base de datos temporal, Multivariate database, Temporal database
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
Department of Computer Science, Applied Computational Intelligence Laboratory-LICAP, Pontifical Catholic University of Minas Gerais, Av. Dom José Gaspar 500, Coração Eucarístico, Belo Horizonte 30535-610, MG, Brazil
ISSN:
0957-4174
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=28843427
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems

Mathematics

Operational research. Management
Accession Number:
edscal.28843427
Database:
PASCAL Archive

Weitere Informationen

This work proposes a new approach for the characterization of time series in databases (temporal databases ― TDB) for temporal analysis of clusters. For the characterization of time-series it were used the level and trend components calculated through the Holt-Winters smoothing model. For the temporal analysis of those clusters, it was used in a combined manner the AGNES (Agglomerative Hierarchical Cluster) and PAM (Partition Clustering) techniques. For the application of this methodology an R-based script for generating synthetic TDBs was developed. Our proposal allows the evaluation of the clusters, both in the object movement such as in the appearance or disappearance of clusters. The model chosen to characterize the time-series is adequate because it can be applied for short periods of time in situations where changes should be promptly detected for quick decision making.