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

Exponential smoothing for time series with outliers

Recursive time series methods are very popular due to their numerical simplicity. Their theoretical background is usually based on Kalman filtering in state space models (mostly in dynamic linear systems). However, in time series practice one must face frequently to outlying values (outliers), which require applying special methods of robust statistics. In the paper a simple robustification of ...

Exponential smoothing for irregular time series

The paper deals with extensions of exponential smoothing type methods for univariate time series with irregular observations. An alternative method to Wright’s modification of simple exponential smoothing based on the corresponding ARIMA process is suggested. Exponential smoothing of order m for irregular data is derived. A similar method using a DLS (discounted least squares) estimation of pol...

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

A Fuzzy-Wavelet Method for Analyzing Non-Stationary Time Series

Fuzzy rule based systems are increasingly being used to deal with time series processes that may lack stochastic stability due to non-stationarity, multiscaling and persistent autocorrelations. Wavelet filtering can be used to deal with such phenomenon. A method for creating a fuzzy-rule base from a time series, where the first difference (returns) of the preprocessed series is used, and high f...

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...