Smoothing of climate time series revisited

Smoothing of climate time series revisited

[1] We present an easily implemented method for smoothing climate time series, generalizing upon an approach previously described by Mann (2004). The method adaptively weights the three lowest order time series boundary constraints to optimize the fit with the raw time series. We apply the method to the instrumental global mean temperature series from 1850–2007 and to various surrogate global m...

On smoothing potentially non-stationary climate time series

[1] A simple approach to the smoothing of a potentially non-stationary time series is presented which provides an optimal choice among three alternative, readily motivated and easily implemented boundary constraints. This method is applied to the smoothing of the instrumental Northern Hemisphere (NH) annual mean and coldseason North Atlantic Oscillation (NAO) time series, yielding an objective ...

A Comparison Between Time Series, Exponential Smoothing, and Neural Network Methods To Forecast GDP of Iran

Optimal Spline Smoothing of FMRI Time Series

Smoothing splines with generalized cross-validation parameter selection (GCV-spline) provide a method to find an optimal smoother for an fMRI time series. The purpose of this study was to compare the variance of parameter estimates and the bias of the variance estimator for a linear regression model smoothed with GCV-spline and the low-pass filter in SPM99 (SPM-HRF). The mean bias with the SPM-...

Robust exponential smoothing of multivariate time series

Multivariate time series may contain outliers of different types. In the presence of such outliers, applying standard multivariate time series techniques becomes unreliable. A robust version of multivariate exponential smoothing is proposed. The method is affine equivariant, and involves the selection of a smoothing parameter matrix by minimizing a robust loss function. It is shown that the rob...