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A hybrid forecasting approach using ARIMA models and self-organising fuzzy neural networks for capital markets

McDonald, S, Coleman, SA, McGinnity, TM and Li, Y 2013, A hybrid forecasting approach using ARIMA models and self-organising fuzzy neural networks for capital markets , in: International Joint Conference on Neural Networks, 4-9 August 2013, Dallas, US.

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Abstract

Linear time series models, such as the autoregressive integrated moving average (ARIMA) model, are among the most popular statistical models used to forecast time series. In recent years non-linear computational models, such as artificial neural networks (ANN), have been shown to outperform traditional linear models when dealing with complex data, like financial time series. This paper proposes a novel hybrid forecasting model which exploits the linear modelling strengths of the ARIMA model, and the flexibility of a self-organising fuzzy neural network (SOFNN). The system's performance is evaluated using several datasets, and our results indicate that a hybrid system is an effective tool for time series forecasting.

Item Type: Conference or Workshop Item (Paper)
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Neural Networks (IJCNN), The 2013 International Joint Conference
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Refereed: Yes
Related URLs:
Funders: Non funded research
Depositing User: Yuhua Li
Date Deposited: 27 Jul 2015 10:58
Last Modified: 05 Apr 2016 18:18
URI: http://usir.salford.ac.uk/id/eprint/33109

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