Result: Employing local modeling in machine learning based methods for time-series prediction

Title:
Employing local modeling in machine learning based methods for time-series prediction
Authors:
WU, Shin-Fu, LEE, Shie-Jue
Source:
Expert systems with applications. 42(1):341-354
Publisher Information:
Amsterdam: Elsevier, 2015.
Publication Year:
2015
Physical Description:
print, 3/4 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, Sélection et gestion de portefeuilles, Portfolio theory, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Logiciel, Software, Organisation des mémoires. Traitement des données, Memory organisation. Data processing, Systèmes d'information. Bases de données, Information systems. Data bases, Intelligence artificielle, Artificial intelligence, Connexionnisme. Réseaux neuronaux, Connectionism. Neural networks, Apprentissage(intelligence artificielle), Learning (artificial intelligence), Classification à vaste marge, Vector support machine, Máquina ejemplo soporte, Evaluation performance, Performance evaluation, Evaluación prestación, Finance, Finanzas, Historique, Case history, Estudio histórico, Information mutuelle, Mutual information, Información mutual, Intelligence artificielle, Artificial intelligence, Inteligencia artificial, Interrogation base donnée, Database query, Interrogación base datos, Modélisation, Modeling, Modelización, Méthode adaptative, Adaptive method, Método adaptativo, Méthode moindre carré, Least squares method, Método cuadrado menor, Plus proche voisin, Nearest neighbour, Vecino más cercano, Prévision, Forecasting, Previsión, Recherche locale, Local search, Busca local, Réseau neuronal, Neural network, Red neuronal, Sensibilité contexte, Context aware, Sensibilidad contexto, Série temporelle, Time series, Serie temporal, Théorie locale, Local theory, Teoría local, Réseau neuronal flou, Fuzzy neural nets, Red neuronal difusa, Local modeling, Machine learning, Nearest neighbors, Time series prediction
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung 804, Tawain, Province of China
ISSN:
0957-4174
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=28843406
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.28843406
Database:
PASCAL Archive

Further Information

Time series prediction has been widely used in a variety of applications in science, engineering, finance, etc. There are two different modeling options for constructing forecasting models in time series prediction. Global modeling constructs a model which is independent from user queries. On the contrary, local modeling constructs a local model for each different query from the user. In this paper, we propose a local modeling strategy and investigate the effectiveness of incorporating local modeling with three popular machine learning based forecasting methods, Neural Network (NN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Least Squares Support Vector Machine (LS-SVM), for time series prediction. Given a series of historical data, a local context of the user query is located and an appropriate number of lags are selected. Then forecasting models are constructed by applying NN, ANFIS, and LS-SVM, respectively. A number of experiments are conducted and the results show that local modeling can enhance the estimation performance of a forecasting method for time series prediction.