Abstract
We survey a number of applications of the wavelet transform in time series prediction. The Haar à trous wavelet transform is proposed as a means of handling time series data when future data is unknown. Results are exemplified on financial futures and S&P500 data. Nonlinear and linear multiresolution autoregressionmodels are studied. Experimentally, we show that multiresolution approaches can outperform the traditional single resolution approach to modeling and prediction.
| Original language | English |
|---|---|
| Pages (from-to) | 475-484 |
| Number of pages | 10 |
| Journal | Decision Support Systems |
| Volume | 37 |
| Issue number | 4 |
| Early online date | 3 Jul 2003 |
| DOIs | |
| Publication status | Published - 1 Sep 2004 |
| Externally published | Yes |
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Murtagh, F., Starck, J. L., & Renuad, O. (2004). On neuro-wavelet modeling. Decision Support Systems, 37(4), 475-484. https://doi.org/10.1016/S0167-9236(03)00092-7