Article publication date: 1 June 2004
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.
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
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