Prediction of stride interval time series

The power law in the frequency spectrum S(f) = 1/f
^β
allows for a good representation of the various time evolution and complex interactions of many physiological processes. The spectral exponent β can be interpreted as the degree of fractal characteristic which in turn makes it some sort of biomarker that gives an idea of the relative health of an individual. The prediction of the 1/f
^β
time series can thus prove to be an asset in the medical field where forecasting the future health state of an individual can be important for rehabilitation purposes. The goal of this paper is to consider the accuracy of several time series prediction methods such as the neural networks, regression trees and bagged regression trees learning method. To test these methods we simulate stride intervals time series as 1/f
^β
processes. Our results show that the regression trees can accurately predict between five and fifteen points.