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Quantify effects of long range memory on predictability of complex systems

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3 Author(s)
Xiaoping Shen ; Dept. of Math., Ohio Univ., Athens, OH, USA ; Farris, K.A. ; Havig, P.R.

This paper explores the connection between uncertainty and memory effects of time series associated with complex system. Traditionally, information theory based algorithms, such as Shannon entropy and its relatives, are employed as measurements to describe uncertainty quantitatively. This study brings into focus the important role of the long range memory effects on the uncertainty measurements. The method is applicable to arbitrary complex systems. Financial data are investigated as an example. The approach provides important insights into the predictability of a complex system.

Published in:

Aerospace and Electronics Conference (NAECON), Proceedings of the 2011 IEEE National

Date of Conference:

20-22 July 2011