Time-Series Forecast with Adaptive Feedback Controlled Predictor
Mark A. Pinsky · Universal Journal of Computational Mathematics · 2013
This paper describes a novel approach to predicting time-series which blends techniques developed in the areas of observer design and numerical solvers for ODEs. The developed predictor is based on a novel feedback control architecture which leads to computationally efficient and a fairly accurate forecast even for volatile economic series. Application to series of various kinds shows that the developed forecaster possesses some basic properties of numerical solvers for ODE. In the same time it prediction horizon is favorably compared with a time step attaining in numerical simulations for the series with precisely known models whereas no knowledge of the series’ global model is assumed in our forecast. We demonstrate that for noisy series the accuracy of prediction reduces to the level of noise to signal ratio as well as that reduction of noise by smoothing the series comparably increases the accuracy of prediction. It is also shown that the developed approach provides practically valuable forecast in application to volatile economic series.