Abstract
Basic statistics of a nonstationary time series are estimated from its single realization. The estimates are represented in the form of a recurrent procedure forming residual time series and smoothing them with the help of effective models of digital data filtering or a locally weighted polynomial regression. The concept of local time weighting and robust weighting of residual series is generalized on the basis of a rational combination of models of distance-weighted least squares and an exponentially weighted regression. The estimation of trends, volatility, and autocorrelation for time series of companies’ sales volumes and the price dynamics of stock assets is simulated, and the results of simulation are presented.
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