Abstract
Several improvements for the local reconstruction method in time series prediction are developed and presented in this paper. More specifically, it is suggested that by augmenting the state vectors with informative features coming from second order
information involving the topology of their neighbouring state vectors then, significantly better results could be obtained with respect to time series reconstruction. Also, the weighting methodology in the least square fitting procedure has been modified aiming at eliminating outliers. The validity of these new ideas is investigated by applying them to the reconstruction task of a time series representing stock price evolution.
information involving the topology of their neighbouring state vectors then, significantly better results could be obtained with respect to time series reconstruction. Also, the weighting methodology in the least square fitting procedure has been modified aiming at eliminating outliers. The validity of these new ideas is investigated by applying them to the reconstruction task of a time series representing stock price evolution.
Original language | English |
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Title of host publication | Proceedings of 16th IMACS World Congress |
Subtitle of host publication | scientific computation, applied mathematics and simulation |
Editors | M. Deville, R. Owens |
Place of Publication | New Brunswick, New Jersey |
Publisher | International Association for Mathematics and Computers in Simulation |
Number of pages | 5 |
Edition | 2000 |
ISBN (Electronic) | 3952207519 |
ISBN (Print) | 9783952207512 |
Publication status | Published - 18 Aug 2000 |
Event | 16th IMACS World Congress - École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland Duration: 21 Aug 2000 → 25 Aug 2000 |
Conference
Conference | 16th IMACS World Congress |
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Country/Territory | Switzerland |
City | Lausanne |
Period | 21/08/00 → 25/08/00 |
Keywords
- Time series prediction
- Financial forecasting