Pasley, A. and Austin, J. (2004) Distribution Forecasting of High Frequency Time Series. Decision Support Systems, 37 (4). pp. 501-513. ISSN 0167-9236
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.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | Copyright © 2004 Elsevier. |
Keywords: | financial forecasting, neural networks, associative memories, probability distribution forecasting, high frequency time series |
Dates: |
|
Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (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 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:1527 |