Time series analysis based on improved kalman filter model

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%. © 2015 SERSC.