Corpus ID: 1223456

Time series forecasting with SOM and local non-linear models-Application to the DAX 30 index prediction

@inproceedings{Dablemont2003TimeSF,
  title={Time series forecasting with SOM and local non-linear models-Application to the DAX 30 index prediction},
  author={Simon Dablemont and Geoffroy Simon and Amaury Lendasse and A. Ruttiens and François Blayo and Michel Verleysen},
  year={2003}
}
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… CONTINUE READING

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References

Publications referenced by this paper.
SHOWING 1-10 OF 11 REFERENCES

Self-Organizing Maps

  • Teuvo Kohonen
  • Computer Science
  • Springer Series in Information Sciences
  • 1995
VIEW 2 EXCERPTS
HIGHLY INFLUENTIAL

D

  • P. H. Franses
  • van Dijk, “Nonlinear Time series models in Empirical Finance”,Cambridge University Press
  • 2000
VIEW 1 EXCERPT

Optimising the widths of radial basis functions

  • Mark J. L. Orr
  • Mathematics, Computer Science
  • Proceedings 5th Brazilian Symposium on Neural Networks (Cat. No.98EX209)
  • 1998
VIEW 1 EXCERPT

Time Series Analysis”,Princeton

  • J. Hamilton
  • 1994
VIEW 2 EXCERPTS