We employ a nonlinear, nonparametric method to model the stochastic behavior of changes in several short and long term U.S. interest rates. We apply a nonlinear autoregression to the series using the locally weighted regression (LWR) estimation method, a nearest-neighbor method, and evaluate the forecasting performance with a measure of root mean square error (RMSE). We compare the forecasting ...

Abstract This paper proposes a linear approximation of the nonlinear Threshold AutoRegressive model. It is shown that there relation between autoregressive order threshold model and its moving average approximation. The main advantage this can be found in extension some theoretical results developed setting to domain. Among them proposed new estimation procedure for models whose performance com...

We examine a Markov Switching model of Singaporean GDP using a combination of formal moment-based tests and informal graphical tests. The tests confirm that the Markov Switching model fits the data better than a linear, autoregressive alternative. The methods are extended to allow us to identify precisely which features of the data are better captured by the non-linear model. The methods descri...

Prediction models based on different concepts have been proposed in recent years. Improving the accuracy of prediction models has remained as a challenging task for researchers. The prediction accuracy depends not only on the model but also on the complexity of the data. Hence, it is important to choose the best model based on the complexity of data in the prediction. The time series prediction...

Non-linear dynamical systems are difficult to control due to the model uncertainties and external disturbances that may occur in these systems. This paper addresses the problem of identification using dynamic neural networks (DNNs) based on genetic algorithm (GA) for nonlinear dynamic systems. Four different dynamic neural networks are used for identification of the same nonlinear dynamic syste...

The prediction of chaotic time series with neural networks is a traditional practical problem of dynamic systems. This paper is not intended for proposing a new model or a new methodology, but to study carefully and thoroughly several aspects of a model on which there are no enough communicated experimental data, as well as to derive conclusions that would be of interest. The recurrent neural n...

Gaussian mixture models are a very successful method for modeling the output distribution of a state in a hidden Markov model (HMM). However, this approach is limited by the assumption that the dynamics of speech features are linear and can be modeled with static features and their derivatives. In this paper, a nonlinear mixture autoregressive model is used to model state output distributions (...

This paper features an analysis of major currency exchange rate movements in relation to the US dollar, as constituted in US dollar terms. Euro, British pound, Chinese yuan, and Japanese yen are modelled using a variety of non-linear models, including smooth transition regression models, logistic smooth transition regressions models, threshold autoregressive models, nonlinear autoregressive mod...

This study examines the benefits of nonlinear time series modelling to improve forecast accuracy of the El Niño Southern Oscillation (ENSO) phenomenon. The paper adopts a smooth transition autoregressive (STAR) modelling framework to assess the potentially regime-dependent dynamics of sea surface temperature anomaly. The results reveal STAR-type nonlinearities in ENSO dynamics, resulting in sup...