The FEXP estimator for potentially non-stationary linear time series

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 ...

Using Wavelets and Splines to Forecast Non-Stationary Time Series

 This paper deals with a short term forecasting non-stationary time series using wavelets and splines. Wavelets can decompose the series as the sum of two low and high frequency components. Aminghafari and Poggi (2007) proposed to predict high frequency component by wavelets and extrapolate low frequency component by local polynomial fitting. We propose to forecast non-stationary process u...

Modelling Non-Linear and Non-Stationary Time Series

A new adaptive exponential smoothing method for non-stationary time series with level shifts

Simple exponential smoothing (SES) methods are the most commonly used methods in forecasting and time series analysis. However, they are generally insensitive to non-stationary structural events such as level shifts, ramp shifts, and spikes or impulses. Similar to that of outliers in stationary time series, these non-stationary events will lead to increased level of errors in the forecasting pr...

Stationary and non-stationary time series

Time series analysis is about the study of data collected through time. The field of time series is a vast one that pervades many areas of science and engineering particularly statistics and signal processing: this short article can only be an advertisement. Hence, the first thing to say is that there are several excellent texts on time series analysis. Most statistical books concentrate on sta...