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

McDonald, Scott, Coleman, Sonya, McGinnity, T. M. and Li, Yuhua ORCID: https://orcid.org/0000-0003-2913-4478 2014. A hybrid forecasting approach using ARIMA models and self-organising fuzzy neural networks for capital markets. Presented at: 2013 International Joint Conference on Neural Networks (IJCNN), Dallas, TX. USA, 4-9 August 2013. The 2013 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 1-7. 10.1109/IJCNN.2013.6706965

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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)
Date Type:	Published Online
Status:	Published
Schools:	Schools > Computer Science & Informatics
Publisher:	IEEE
ISBN:	9782490057573
Date of Acceptance:	9 August 2013
Last Modified:	07 Nov 2022 09:26
URI:	https://orca.cardiff.ac.uk/id/eprint/129137

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