Name
Title | ||
|---|---|---|
A hybrid forecasting approach using ARIMA models and self-organising fuzzy neural networks for capital markets. |
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/IJCNN.2013.6706965 | IJCNN |
Keywords | Field | DocType |
autoregressive moving average processes,financial data processing,forecasting theory,fuzzy neural nets,time series,ANN,ARIMA models,SOFNN,artificial neural networks,autoregressive integrated moving average model,capital markets,hybrid forecasting approach,linear time series models,nonlinear computational models,self-organising fuzzy neural networks,statistical models,system performance evaluation,time series forecasting | Time series,Neuro-fuzzy,Linear model,Computer science,Autoregressive integrated moving average,Computational model,Artificial intelligence,Statistical model,Artificial neural network,Hybrid system,Machine learning | Conference |
ISSN | Citations | PageRank |
2161-4393 | 2 | 0.38 |
References | Authors | |
13 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Scott McDonald | 1 | 2 | 0.38 |
| Sonya Coleman | 2 | 216 | 36.84 |
| T. Martin Mcginnity | 3 | 518 | 66.30 |
| Yuhua Li | 4 | 1113 | 53.63 |