Distribution Forecasting of High Frequency Time Series

J. Austin, A. Pasley

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)501-513
Number of pages13
JournalDecision Support Systems
Issue number4
Publication statusPublished - Sept 2004

Bibliographical note

Copyright © 2004 Elsevier.


  • financial forecasting
  • neural networks
  • associative memories
  • probability distribution forecasting
  • high frequency time series

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