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Distribution Forecasting of High Frequency Time Series

Pasley, A. and Austin, J. (2004) Distribution Forecasting of High Frequency Time Series. Decision Support Systems, 37 (4). pp. 501-513. ISSN 0167-9236

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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

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