Papers by Keyword: Detrended Fluctuation Analysis

Abstract: The fractals of the optimization problems are first discussed. The multi-fractal parameters of the optimal objective function are computed by the Detrended Fluctuation Analysis (DFA) method. The multi-fractal general Hurst Index is related to the difficulty to solve the optimization problem. These features are verified by analyzing the first six test functions proposed on 2005 IEEE Congress on Evolutionary Computation. The results show that the different objective functions have obvious different multifractal and the general Hurst Index can be used to evaluate the difficulty to solve the optimization problem.

Abstract: In this paper, detrended fluctuation analysis is used to calculate the Hurst exponent, the fractal dimensions and finally the climate predictability indices of monthly and seasonal time series of air temperature, surface pressure, precipitation, wind speed and relative humidity for Beijing meteorological stations, in which the meteorological data cover a period from 1951 to 2009 and the precipitation data own a series of 286 years (1724~2009). And we found that at the monthly scale, the predictability of precipitation and wind speed was not controlled by temperature and pressure. A strong negative correlation showed for precipitation VS. temperature and pressure, and the persistence trait of wind speed just depended absolutely on itself. At the seasonal scale, all three meteorological parameters existed negative persistence behavior with temperature and pressure in winter. In spring, the persistence behavior of precipitation is in step with that of temperature and pressure, and for wind speed and relative humidity, it got unconformable results.

Abstract: We propose a new method called the multi-dependent Hurst exponent to investigate the correlation properties of the nonstationary time series. The method is validated with the artificial series including both short-range correlated data and long-range correlated data. The results indicate that the multi-dependent Hurst exponents fluctuate around the a-priori known correlation exponent H. Application to traffic time series is also presented, and comparison is made between the artificial time series and traffic time series.