Distribution forecasting of high frequency time series

Authors:
Andy Pasley;Jim Austin
Affiliations:
Department of Computer Science, University of York, Heslington, York YO10 5DD, UK;Department of Computer Science, University of York, Heslington, York YO10 5DD, UK
Venue:
Decision Support Systems - Special issue: Data mining for financial decision making
Year:
2004

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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.