State estimation for stochastic object on the base of modeling trend in time series

A new model for state recognition in dynamic object provided in the form of non-stationary time series is discussed. Algorithms for construction of generalized etalons of dynamic object state and of modeling segment of time series on the base of generalized etalons and difference scheme are suggested. Availability of the model is shown on an applied problem.