Hybridization of intelligent techniques and ARIMA models for time series prediction

Traditionally, the autoregressive moving average (ARMA) model has been one of the most widely used linear models in time series prediction. Recent research activities in forecasting with artificial neural networks (ANNs) suggest that ANNs can be a promising alternative to the traditional ARMA struct...

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Bibliographic Details
Published in:Fuzzy sets and systems 2008-04, Vol.159 (7), p.821-845
Main Authors: Valenzuela, O., Rojas, I., Rojas, F., Pomares, H., Herrera, L.J., Guillen, A., Marquez, L., Pasadas, M.
Format: Article
Language:English
Subjects:
Applied sciences
ARIMA models
Circuit properties
Computer science
control theory
systems
Control theory. Systems
Digital circuits
Electric, optical and optoelectronic circuits
Electronic circuits
Electronics
Exact sciences and technology
Fuzzy systems
Hybrid system
Information, signal and communications theory
Mathematical methods
Miscellaneous
Neural networks
System theory
Telecommunications and information theory
Theoretical computing
Time series
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Summary:Traditionally, the autoregressive moving average (ARMA) model has been one of the most widely used linear models in time series prediction. Recent research activities in forecasting with artificial neural 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. In this paper we propose a hybridization of intelligent techniques such as ANNs, fuzzy systems and evolutionary algorithms, so that the final hybrid ARIMA–ANN model could outperform the prediction accuracy of those models when used separately. More specifically, we propose the use of fuzzy rules to elicit the order of the ARMA or ARIMA model, without the intervention of a human expert, and the use of a hybrid ARIMA–ANN model that combines the advantages of the easy-to-use and relatively easy-to-tune ARIMA models, and the computational power of ANNs.
ISSN:0165-0114
1872-6801
DOI:10.1016/j.fss.2007.11.003