Improving recurrent forecasting in singular spectrum analysis using Kalman filter algorithm
نویسندگان
1 Department of Statistics, Payame Noor University, Tehran, Iran
2 Department of Statistics, Payame Noor University, Tehran, Iran
3 Research Institute for Energy Management and Planning, University of Tehran, Iran
doi
10.22034/jsmta.2023.19362.1078چکیده
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.
