improving recurrent forecasting in singular spectrum analysis usingkalman filter algorithm

One of the most practical nonparametric methods in the analysis of timeseries observations is the singular spectrum analysis (ssa) method. this method hasbeen developed and applied to many practical problems across different fields and continuousefforts have been made to improve this method, especially in forecasting. inthis paper the state space model and kalman filter algorithms are used for noise eliminationand time series smoothing. finally, we compare these forecasting methods’abilities using the root mean squared error criterion (rmse) for simulation studiesand real data.