Improving recurrent forecasting in singular spectrum analysis using Kalman filter algorithm

Document Type : Original Scientific Paper

Authors

1 Department of Statistics, Payame Noor University, Tehran, Iran

2 Research Institute for Energy Management and Planning, University of Tehran, Tehran, Iran

Abstract

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.

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References

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Journal of Statistical Modelling: Theory and Applications
Volume 3, Issue 1
January 2022
Pages 135-146

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History

  • Receive Date: 07 December 2022
  • Revise Date: 17 March 2023
  • Accept Date: 19 March 2023

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