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. (Request a copy)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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