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 Sept 2004 |
| Externally published | Yes |