Predicting time series with advanced hybrid systems

Authors:
O. Valenzuela;I. Rojas;F. Rojas;H. Pomares;J. Gonzalez;L. J. Herrera;A. Guillen
Affiliations:
Department of Applied Mathematic, University of Granada, Spain;Department of computer Architecture and computer Technology, University of Granada, Spain;Department of computer Architecture and computer Technology, University of Granada, Spain;Department of computer Architecture and computer Technology, University of Granada, Spain;Department of computer Architecture and computer Technology, University of Granada, Spain;Department of computer Architecture and computer Technology, University of Granada, Spain;Department of computer Architecture and computer Technology, University of Granada, Spain
Venue:
MATH'05 Proceedings of the 8th WSEAS International Conference on Applied Mathematics
Year:
2005

Citing 2
Cited 0

Time Series Analysis: Forecasting and Control

Time Series Analysis: Forecasting and Control
Fitting ARMA Models to linear non-Gaussian processes using higher order statistics

Signal Processing - Image and Video Coding beyond Standards

Quantified Score

Hi-index	0.00

Visualization

Abstract

Autogressive moving average (ARMA) has been widely used to model processes that generate linear time-series. Recent research activities in forecasting with artificial neutral networks (ANNs) suggest that ANNs can be a promising alternative to the traditional ARMA structure. These linear models and ANNs are often compared with mixed conclusions in terms of the superiority in forecasting performance. This study was designed: a) to investigate a hybrid methodology that combines ANN and ARMA models; b) to resolve one of the most important problems in time series using ARMA structure and Box-Jenkins methodology, the identification of the model. In this paper we present a new procedure to predict time series using paradigms as: fuzzy system, neutral networks and evolutionary algorithm. Our goal is to obtain an expert system based on paradigms of artificial intelligence, so that the linear model can be identified automatically, without the necessity for a human expert to intervene. The obtained linear model will be combine with ANN, making and hybrid system that could outperform the forecasting result.