To read the full version of this content please select one of the options below:

Forecast entropy

W. Yao (Department of Applied Mathematics, University of Western Ontario, London, Ontario, Canada)
C. Essex (Department of Applied Mathematics, University of Western Ontario, London, Ontario, Canada)
P. Yu (Department of Applied Mathematics, University of Western Ontario, London, Ontario, Canada)
M. Davison (Department of Applied Mathematics, University of Western Ontario, London, Ontario, Canada)

Kybernetes

ISSN: 0368-492X

Article publication date: 1 June 2004

Abstract

A technique, called forecast entropy, is proposed to measure the difficulty of forecasting data from an observed time series. When the series is chaotic, this technique can also determine the delay and embedding dimension used in reconstructing an attractor. An ideal random system is defined. An observed time series from the Lorenz system is used to show the results.

Keywords

Citation

Yao, W., Essex, C., Yu, P. and Davison, M. (2004), "Forecast entropy", Kybernetes, Vol. 33 No. 5/6, pp. 1009-1015. https://doi.org/10.1108/03684920410534056

Publisher

:

Emerald Group Publishing Limited

Copyright © 2004, Emerald Group Publishing Limited