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

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)
Date Type:	Published Online
Status:	Published
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

Citation Data

Cited 5 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item