Amazon.com: Non-Linear Time Series: A Dynamical System Approach Oxford Statistical Science Series, 6 9780198523000: Howell Tong: Books.nonlinear time series model from a nonlinear stochastic dynamical system. Only the approach of Smith 1977 based on piecewise linear.Time series analysis for nonlinear dynamical systems with applications to. Vides a computationally viable alternative to a state-o...

this paper presents a fuzzy approach to the prediction of highly nonlinear timeseries.the optimized mamdani-type fuzzy system denoted sqp-flc is applied forthe input-output modeling of measured data. in order to tune fuzzy membershipfunctions, a sequential quadratic programming (sqp) method is employed. theproposed method is evaluated and validated on a highly complex time series, dailygold pri...

In this paper statistical and time series models are used for determining the random drift of a dynamically Tuned Gyroscope (DTG). This drift is compensated with optimal predictive transfer function. Also nonlinear neural-network and fuzzy-neural models are investigated for prediction and compensation of the random drift. Finally the different models are compared together and their advantages a...

Background and Objectives: Efficient cost management in hospitals’ pharmaceutical inventories have the potential to remarkably contribute to optimization of overall hospital expenditures. To this end, reliable forecasting models for accurate prediction of future pharmaceutical demands are instrumental. While the linear methods are frequently used for forecasting purposes chiefly due to their si...

Building upon Beaudry and Koop’s (1993) analysis, we consider a “current depth of the recession” (CDR) variable in modeling the time-series behavior of the postwar quarterly U.S. unemployment rate. The CDR approach is consistent with the state-dependent behavior in the unemployment rate documented in the business-cycle asymmetry literature. We show that while the CDR effect is significant in-sa...