Improving recurrent forecasting in singular spectrum analysis using Kalman filter algorithm

One of the most practical nonparametric methods in analysis of time series observations is the singular spectrum analysis method‎. ‎This method has been developed and applied to many practical problems across different fields and continuous efforts have been made to improve this method‎, ‎especially in forecasting‎. ‎In this paper‎, ‎the state space model and Kalman filter algorithms are used for noise elimination and time series smoothing‎. ‎Finally‎, ‎we compare these forecasting methods' abilities using the root mean squared error criteria for simulation studies and the real datasets.