원문정보
초록
영어
In common time series analysis methods, the prediction accuracy of the low-order model is poor, and the high-order model is difficult to calculate. Therefore, in this paper, we improve the construction process of the Kalman filtering model, and apply it into time series analysis. The concrete implementation for the improved method is to construct the low-order model with the ARMA method and intercept sufficient delay states, to deduce the state equation and measurement equation of the Kalman filtering model. As the experimental results show that,the improved Kalman filtering model can not only simplify the derivation of the state equation and measurement equation, but also achieve ideal prediction accuracy, the largest prediction error of the experimental data is -0.15%.
목차
1. Introduction
2. Construction of the Improved Kalman Filtering Model
2.1 Construction Time Series Low-Order Model with the ARMA Method
2.2 Construction of Kalman Model Based on the ARMA Low-Order Model
3. Experimental Results and Analysis
4. Conclusion
References