Persistence in Nonlinear Time Series: A Nonparametric Approach

Nonlinear Time Series: Nonparametric and Parametric Methods

Time Series Forecasting: a Nonlinear Dynamics Approach

The problem of prediction of a given time series is examined on the basis of recent nonlinear dynamics theories. Particular attention is devoted to forecast the amplitude and phase of one of the most common solar indicator activity, the international monthly smoothed sunspot number. It is well known that the solar cycle is very difficult to predict due to the intrinsic complexity of the related...

Functional Methods for Time Series Prediction: A Nonparametric Approach

The problem of prediction in time series using nonparametric functional techniques is considered. An extension of the local linear method to regression with functional explanatory variable is proposed. This forecasting method is compared with the functional Nadaraya–Watson method and with fi nitedimensional nonparametric predictors for several real-time series. Prediction intervals based on the...

A Bayesian nonparametric approach for time series clustering

In this work we propose a model-based clustering method for time series. The model uses an almost surely discrete Bayesian nonparametric prior to induce clustering of the series. Specifically we propose a general Poisson-Dirichlet process mixture model, which includes the Dirichlet process mixture model as particular case. The model accounts for typical features present in a time series like tr...

Nonparametric Specification Testing for Nonlinear Time Series with Nonstationarity

This paper considers a nonparametric time series regression model with a nonstationary regressor. We construct a nonparametric test for testing whether the regression is of a known parametric form indexed by a vector of unknown parameters. We establish the asymptotic distribution of the proposed test statistic. Both the setting and the results differ from earlier work on nonparametric time seri...