2019 : Estimating the function of oscillatory components in SSA-based forecasting model
The study of SSA-based forecasting model is always interesting due to its capability in modeling trend and multiple seasonal time series. The aim of this study is to propose an iterative ordinary least square (OLS) for estimating the oscillatory with time-varying amplitude model that usually found in SSA decomposition. We compare the results with those obtained by nonlinear least square based on Levenberg Marquardt (NLM) method. A simulation study based on the time series data which has a linear amplitude modulated sinusoid component is conducted to investigate the error of estimated parameters of the model obtained by the proposed method. A real data series was also considered for the application example. The results show that in terms of forecasting accuracy, the SSA-based model where the oscillatory components are obtained by iterative OLS is nearly the same with that is obtained by the NLM method.