Prediction of NOx concentration time series using the chaos theory

Abstract

This paper is aimed at analysis of NOx concentration time series. At first we estimated the time delay and the embedding dimension, which is needed for the Lyapunov exponent estimation and for the phase space reconstruction. Subsequently we computed the largest Lyapunov exponent, which is one of the important indicators of chaos. Then we estimated the correlation dimension and Kolmogorov entropy. The results indicated that chaotic behaviors obviously exist in NOx concentration time series. Finally we computed predictions using a radial basis function and polynomials to fit global nonlinear functions to the data.