Strategies for Modelling Nonlinear Time-Series Relationships

NONLINEAR TIME SERIES MODELLING: ORDER IDENTIFICATION AND WAVELET FILTERING Abbreviated title: Nonlinear time series modelling

In this paper we discuss an approach for modelling nonlinear time series data based on wavelet smoothing. The technique involves decomposing the series into two components a deterministic component which when extracted by wavelet ̄ltering leaves a random component which can be easily modelled using well known linear time series modelling techniques or by a simple diagonal pure bilinear model dis...

Modelling of nonlinear filtering Poisson time series

In this article, algorithms of non-linear filtering of Poisson time series are tested using statistical modelling. The objective is to find a representation of a time series as a wavelet series with a small number of non-linear coefficients, which allows distinguishing statistically significant details. There are well-known efficient algorithms of non-linear wavelet filtering for the case when ...

Nonlinear modelling of air pollution time series

An analysis of predictability of a nonlinear and nonstationary ozone time series is provided. For rigour, the DVS analysis is first undertaken to detect and measure inherent nonlinearity of the data. Based upon this, neural and linear adaptive predictors are compared on this time series for various filter orders, hence indicating the embedding dimension. Simulation results confirm the analysis ...

Testing linearity and modelling nonlinear time series

Nonlinear Time Series Modelling: An Introduction

Recent developments in nonlinear time series modelling are reviewed. Three main types of nonlinear model are discussed: Markov Switching, Threshold Autoregression and Smooth Transition Autoregression. Classical and Bayesian estimation techniques are described for each model. Parametric tests for nonlinearity are reviewed with examples from the three types of model. Finally forecasting and impul...