On the Estimation of Time Varying AR Processes

Abstract In time series analysis auto regressive (AR) modelling of zero mean data is widely used for system identification, signal decorrelation, detection of outliers and forecasting. An AR process of order p is uniquely defined by r coefficients and the variance of the noise. The roots of the characteristic polynomial can be used as an alternative parametrization of the coefficients, which is used to construct a continuous covariance function of the AR processes or to verify that the AR processes are stationary. In this contribution we propose an approach to estimate an AR process of time varying coefficients (TVAR process). In the literature, roots are evaluated at discrete times, rather than a continuous function like we have for time varying systems. By introducing the assumption that the movement of the roots are linear functions in time, stationarity for all possible epochs in the time domain is easy to accomplish. We will illustrate how this assumption leads to TVAR coefficients where the k -th coefficient is a polynomial of order k with further restrictions on the parameters of the coefficients. At first we study how to estimate TVAR process parameters by using a Least Squares approach in general. As any AR process can be rewritten as a combination of AR processes of order two with two complex conjugated roots and AR processes of order one, we limit our investigations to these orders. Higher order TVAR processes are computed by successively estimating TVAR processes of orders one or two. Based on a simulation, we will demonstrate the advantages of a time varying model and compare them to the stationary time stable model. In addition, we will give a method to identify time series, for which the model of the TVAR processes with linear roots is suitable.