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.

Full text not available from this repository.

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: 06 Sep 2021 07:40
URI: http://usir.salford.ac.uk/id/eprint/33109

Actions (login required)

Edit record (repository staff only) Edit record (repository staff only)