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.
Yarmohammadi, M., Zabihi Moghadam, R., & Hassani, H. (2023). Improving recurrent forecasting in singular spectrum analysis using Kalman filter algorithm. Journal of Statistical Modelling: Theory and Applications, 3(1), 135-146. doi: 10.22034/jsmta.2023.19362.1078
MLA
Masoud Yarmohammadi; Reza Zabihi Moghadam; Hossein Hassani. "Improving recurrent forecasting in singular spectrum analysis using Kalman filter algorithm", Journal of Statistical Modelling: Theory and Applications, 3, 1, 2023, 135-146. doi: 10.22034/jsmta.2023.19362.1078
HARVARD
Yarmohammadi, M., Zabihi Moghadam, R., Hassani, H. (2023). 'Improving recurrent forecasting in singular spectrum analysis using Kalman filter algorithm', Journal of Statistical Modelling: Theory and Applications, 3(1), pp. 135-146. doi: 10.22034/jsmta.2023.19362.1078
VANCOUVER
Yarmohammadi, M., Zabihi Moghadam, R., Hassani, H. Improving recurrent forecasting in singular spectrum analysis using Kalman filter algorithm. Journal of Statistical Modelling: Theory and Applications, 2023; 3(1): 135-146. doi: 10.22034/jsmta.2023.19362.1078
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