一种短时序Kalman滤波决策优化预测新方法

A New Decision Optimization Prediction Method Based on Short-Term Time Series and Kalman Filter

摘要: 为提升决策的敏捷性和科学性，提出了一种短时间序列和Kalman滤波相结合的决策优化预测方法TS_KF(Time Series-Kalman Filter)。此方法以提高预测准确度和减小计算复杂度为目的，采用Kalman滤波模型对预测过程进行建模，应用时间序列的自回归模型对Kalman滤波进行状态转移的更新和优化。以煤产量预测为例进行方法验证，结果表明，同其它典型的预测方法相比，TS_KF预测方法在保持低计算复杂度的前提下实现了预测准确度的大幅度提升，证明了TS_KF方法的有效性。

Abstract: In order to make scientific and agile decision, a new short-term prediction TS_KF (Time Se-ries-Kalman Filter) method is proposed based on the combination of time series analysis and Kalman Filter. Aiming to improve the prediction precision and decrease the calculation complexity, a prediction model is built using Kalman filter to describe the prediction process, and the auto regression for time series analysis is utilized to renew and optimize the state transfer matrix, which is the key parameter for Kalman Filter. A coal production prediction test is conducted by comparison with some typical time series prediction method, and the results show that the TS_KF prediction method in this paper has significantly enhanced the prediction precision while keeping the same low calculation complexity. The result gives a strong proof for the effectiveness of the new TS_KF prediction method.