University of Hertfordshire

Input window size and neural network predictors

Research output: Chapter in Book/Report/Conference proceedingConference contribution


  • 900909

    Accepted author manuscript, 37 KB, PDF document

  • R. Frank
  • N. Davey
  • Stephen Hunt
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Original languageEnglish
Title of host publicationProcs of the IEEE-INNS-ENNS Int Joint Conf on Neural Networks, 2000 (IJCNN 2000)
ISBN (Print) 0-7695-0619-4
Publication statusPublished - 2000


Neural network approaches to time series prediction are briefly discussed, and the need to specify an appropriately sized input window identified. Relevant theoretical results from dynamic systems theory are briefly introduced, and heuristics for finding the correct embedding dimension, and hence window size, are discussed. The method is applied to two time series and the resulting generalisation performance of the trained feedforward neural network predictors is analysed. It is shown that the heuristics can provide useful information in defining the appropriate network architecture

ID: 422507