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| Titel: |
Time-series analysis with neural networks and ARIMA-neural network hybrids |
| Auteur: |
Hansen, James V. Nelson, Ray D. |
| Verschenen in: |
Journal of experimental & theoretical artificial intelligence |
| Paginering: | Jaargang 15 (2003) nr. 3 pagina's 315-330 |
| Jaar: |
2003-07 |
| Inhoud: |
Time-series analysis is important to a wide range of disciplines transcending both the physical and social sciences. Statistical models have sound theoretical bases and have been successfully used in a number of problem domains. More recently, machine-learning models such as neural networks have been suggested as offering potential for time-series analysis. Results of neural network empirical testing have thus far been mixed. This paper proposes melding useful parameters from the statistical ARIMA model with neural networks of two types: multilevel perceptrons (MLPs) and radial basis functions (RBFs). Tests are run on a range of time-series problems that exhibit many common patterns encountered by analysts. The results suggest that hybrids of the type proposed may yield better outcomes than either model by itself. |
| Uitgever: |
Taylor & Francis |
| Bronbestand: |
Elektronische Wetenschappelijke Tijdschriften |
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