Hurst exponent estimation using neural network

Abstract

The Hurst exponent is used to identify the autocorrelation structure of a stochastic time series, which allows for detecting persistence in time series data. Traditional signal processing techniques work reasonably well in determining the Hurst exponent of a stochastic time series. However, a notable drawback of these methods is their speed of computation. Neural networks have repeatedly proven their ability to learn very complex input-output mappings, even in high dimensional vector spaces. Therefore, an endeavour has been undertaken to employ neural networks to determine the Hurst exponent of a stochastic time series. Unlike previous attempts to solve such problems using neural networks, the proposed architecture can be recognised as the universal estimator of Hurst exponent for short-range and long-range dependent stochastic time series. Experiments demonstrate that if sufficiently trained, neural network can predict the Hurst exponent of any stochastic data at least fifteen times faster than standard signal processing approaches.