Volume 110, Number 1, April 2015
|Number of page(s)||5|
|Published online||16 April 2015|
Improving predictability of time series using maximum entropy methods
1 Department of Computer Science, University of Geneva - Geneva 1227, Switzerland
2 Department of Theoretical Physics, University of Geneva - Geneva 1211, Switzerland
3 Olsen Ltd. - Zurich 8008, Switzerland
Received: 9 December 2014
Accepted: 25 March 2015
We discuss how maximum entropy methods may be applied to the reconstruction of Markov processes underlying empirical time series and compare this approach to usual frequency sampling. It is shown that, in low dimension, there exists a subset of the space of stochastic matrices for which the MaxEnt method is more efficient than sampling, in the sense that shorter historical samples have to be considered to reach the same accuracy. Considering short samples is of particular interest when modelling smoothly non-stationary processes, which provides, under some conditions, a powerful forecasting tool. The method is illustrated for a discretized empirical series of exchange rates.
PACS: 02.50.Ey – Stochastic processes / 02.50.Tt – Inference methods / 89.65.Gh – Economics; econophysics, financial markets, business and management
© EPLA, 2015
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