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Paper   IPM / M / 8864
School of Mathematics
  Title:   Multistep forecasting non-stationary time series using wavelets and kernel smoothing
  Author(s):  M. Aminghafari (Joint with J-M Poggi)
  Status:   In Proceedings
  Proceeding: Proceedings of the 28th international symposium on forecasting
  Year:  2008
  Supported by:  IPM
This work deals with forecasting time series using wavelets and ker�nel smoothing. A forecasting procedure can be defined by estimating the prediction equation by direct regression of the process on the non�decimated wavelet coefficients depending on its past values. In the same context, after the seminal work of Renaud et al. [RSM03], we study a generalization of the prediction procedure associating kernel smoothing and wavelets. We then illustrate the proposed procedure on non-stationary simulated and real data.

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