Functional methods for time series prediction: a nonparametric approach

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...

Some New Methods for Prediction of Time Series by Wavelets

Extended Abstract. Forecasting is one of the most important purposes of time series analysis. For many years, classical methods were used for this aim. But these methods do not give good performance results for real time series due to non-linearity and non-stationarity of these data sets. On one hand, most of real world time series data display a time-varying second order structure. On th...

Nonparametric Wavelet Methods for Nonstationary Time Series

This article gives an overview on nonparametric modelling of nonstationary time series and estimation of their time-changing spectral content by modern denoising (smoothing) methods. For the modelling aspect localized decompositions such as various local Fourier (spectral) representations are discussed, among which wavelet and local cosine bases are most prominent ones. For the estimation of th...

Nonparametric sequential prediction of time series

Time series prediction covers a vast field of every-day statistical applications in medical, environmental and economic domains. In this paper we develop nonparametric prediction strategies based on the combination of a set of “experts” and show the universal consistency of these strategies under a minimum of conditions. We perform an indepth analysis of real-world data sets and show that these...

Nonlinear Time Series: Nonparametric and Parametric Methods