Pasley, A. and Austin, J. (2004) Distribution Forecasting of High Frequency Time Series. Decision Support Systems, 37 (4). pp. 501-513. ISSN 0167-9236
Full text not available from this repository.Abstract
The availability of high frequency data sets in finance has allowed the use of very data intensive techniques using large data sets in forecasting. An algorithm requiring fast k-NN type search has been implemented using AURA, a binary neural network based upon Correlation Matrix Memories. This work has also constructed probability distribution forecasts, the volume of data allowing this to be done in a nonparametric manner. In assistance to standard statistical error measures the implementation of simulations has allowed actual measures of profit to be calculated.
| Item Type: | Article |
|---|---|
| Copyright, Publisher and Additional Information: | Copyright © 2004 Elsevier. |
| Keywords: | financial forecasting, neural networks, associative memories, probability distribution forecasting, high frequency time series |
| Academic Units: | The University of York > Computer Science (York) |
| Depositing User: | Sherpa Assistant |
| Date Deposited: | 01 Sep 2006 |
| Last Modified: | 05 Aug 2007 18:17 |
| Published Version: | http://dx.doi.org/10.1016/S0167-9236(03)00083-6 |
| Status: | Published |
| Publisher: | Elsevier |
| Refereed: | Yes |
| Identification Number: | 10.1016/S0167-9236(03)00083-6 |
| URI: | http://eprints.whiterose.ac.uk/id/eprint/1527 |
Actions (login required)
 |
View Item |
