Time series forecasting with SOM and local non-linear models - Application to the DAX30 index prediction
(2003) WSOM 2003, Workshop on Self-Organizing Maps — Location: Hibikino (Japan) (11.September.2003)
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- Authors
- Abstract
- A general method for time series forecasting is presented. Based on the splitting of the past dynamics into clusters, local models are built to capture the possible evolution of the series given the last known values. A probabilistic model is used to combine the local predictions. The method can be applied to any time series prediction problem, but is particularly suited to data showing non-linear dependencies and cluster effects, as many financial series do. The method is applied to the prediction of the returns of the DAX30 index.
- Affiliations
CBC Banque PREFIGURE Espace G2C, Lyon
Citations
Dablemont, S., Simon, G., Lendasse, A., Ruttiens, A., Blayo, F., & Verleysen, M. (2003). Time series forecasting with SOM and local non-linear models - Application to the DAX30 index prediction. Proceedings of WSOM 2003, Workshop on Self-Organizing Maps, p. 340-345. https://hdl.handle.net/2078.5/226204