This work is concerned with nonlinear time series models and, in particular, with nonparametric models for the dynamics of the mean of the time series. We build on the functional-coefficient autoregressive (FAR) model of Chen and Tsay (1993) which is a generalization of the autoregressive (AR) model where the coefficients are varying and are given by functions of the lagged values of the series...

Nowadays a great deal of effort has been made in order to gain advantages in foreign exchange (FX) rates predictions. However, most existing techniques seldom excel the simple random walk model in practical applications. This paper describes a self-organising network formed on the basis of a mixture of adaptive autoregressive models. The proposed network, termed self-organising mixture autoregr...

Energy consumption time series consists of complex linear and non-linear patterns and are difficult to forecast. Neither autoregressive integrated moving average (ARIMA) nor artificial neural networks (ANNs) can be adequate in modeling and predicting energy consumption. The ARIMA model cannot deal with nonlinear relationships while the neural network model alone is not able to handle both linea...

Both theoretical and empirical findings have suggested that combining different models can be an effective way to improve the predictive performance of each individual model. It is especially occurred when the models in the ensemble are quite different. Hybrid techniques that decompose a time series into its linear and nonlinear components are one of the most important kinds of the hybrid model...

Residuals are frequently used as a diagnostic tool for verification that a time series model fits to data. In the cases when the series is nonnormal and/or the model is nonlinear, the squared residuals and squared values of the series are taken into account. In our paper asymptotic formulas for the mean value and variance of the corresponding sample corre­ lation functions are calculated. Small...

We consider nonlinear and heteroscedastic autoregressive models whose residuals are martingale increments with conditional distributions that fulfill certain constraints. We treat two classes of constraints: residuals depending on the past through some function of the past observations only, and residuals that are invariant under some finite group of transformations. We determine the efficient ...

Dynamic textures are sequences of images that exhibit some form of temporal stationarity, such as waves, steam, and foliage. We pose the problem of recognizing and classifying dynamic textures in the space of dynamical systems where each dynamic texture is uniquely represented. Since the space is non-linear, a distance between models must be defined. We examine three different distances in the ...

This paper analyzes the absolute convergence of urban electricity productivity in China during 1990–2011, applying Spatial Error Smooth Transition Auto-regression Model, and examines the spatial effect in convergence process. Then it analyzes the stage characteristics of electricity productivity convergence during the process of electricity market reformation and the conditional convergence whi...

We extend the functional coefficient autoregressive (FCAR) model to the multivariate nonlinear time series framework. We show how to estimate parameters of the model using kernel regression techniques, discuss properties of the estimators, and provide a bootstrap test for determining the presence of nonlinearity in a vector time series. The power of the test is examined through extensive simula...