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

McDonald, S; Coleman, SA; McGinnity, TM; Li, Y

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.

Citation

McDonald, S., Coleman, S., McGinnity, T., & Li, Y. (2013, August). A hybrid forecasting approach using ARIMA models and self-organising fuzzy neural networks for capital markets. Presented at International Joint Conference on Neural Networks, Dallas, US

• Presentation Conference Type: Other
• Conference Name: International Joint Conference on Neural Networks
• Conference Location: Dallas, US
• Start Date: Aug 4, 2013
• End Date: Aug 9, 2013
• Publication Date: Aug 4, 2013
• Deposit Date: Jul 27, 2015
• Publisher: Institute of Electrical and Electronics Engineers
• Publisher URL: http://dx.doi.org/10.1109/IJCNN.2013.6706965
• Related Public URLs: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6691896
• Additional Information: Event Type : Conference