scholarly journals Non-parametric Estimation of Stochastic Differential Equations from Stationary Time-Series

2021 ◽  
Vol 186 (1) ◽  
Author(s):  
Xi Chen ◽  
Ilya Timofeyev
Keyword(s):  
Time Series ◽  
Differential Equations ◽  
Stochastic Differential Equations ◽  
Parametric Estimation ◽  
Stationary Time Series ◽  
Stationary Time ◽  
Non Parametric Estimation ◽  
Non Parametric

A New Hybrid-Multiscale SSA Prediction of Non-Stationary Time Series

2016 ◽  
Vol 15 (01) ◽  
pp. 1650005 ◽  
Author(s):  
Mitra Ghanbarzadeh ◽  
Mina Aminghafari
Keyword(s):  
Time Series ◽  
Parametric Method ◽  
Real Data ◽  
Stationary Time Series ◽  
Correlated Noise ◽  
Wavelet Regression ◽  
Stationary Time ◽  
Determination Of Parameters ◽  
Two Parameters ◽  
Non Parametric

Singular spectral analysis (SSA) is a non-parametric method used in the prediction of non-stationary time series. It has two parameters, which are difficult to determine and very sensitive to their values. Since, SSA is a deterministic-based method, it does not give good results when the time series is contaminated with a high noise level and correlated noise. Therefore, we introduce a novel method to handle these problems. It is based on the prediction of non-decimated wavelet (NDW) signals by SSA and then, prediction of residuals by wavelet regression. The advantages of our method are the automatic determination of parameters and taking account of the stochastic structure of time series. As shown through the simulated and real data, we obtain better results than SSA, a non-parametric wavelet regression method and Holt–Winters method.

Sign in / Sign up

Export Citation Format

Share Document