卡尔曼实时跟踪模型在股票价格预测中的应用

基于卡尔曼滤波的动态、实时跟踪性以及股票市场的易波动性,该文提出了将股票视为一个机动物体,其价格视为该物体的位移,其价格的变化视为该物体的速度,依据非线性物理动力学模型来描述股票价格的波动,并且利用卡尔曼滤波理论建立了一种动态的股票价格预测模型,最后给出了相应的算法.通过实例仿真,并对结果进行分析表明,本文提出的算法具有可靠、计算简便、快速等特点,模型预测精度较高,并可实现实时跟踪预测,具有一定的理论价值和实用价值.

Application of A Real-time Tracking Model Based on Kalman Filter in The Prediction of Stock Price

Based on the dynamic and real-time tracking features of Kalman filter and the fluctuation of the stock market,the stock is viewed as a maneuvering object and its price is viewed as the displacement of the object and the change of the price is viewed as the velocity of the object in this article.Then,the nonlinear dynamic model is used to describe the fluctuation of the stock price,and a dynamic prediction model of stock price is established with the theory of Kalman filter.The corresponding algorithm is given at last.The result of the instance simulation indicates that the algorithm posed in this article is reliable,simple and rapid,and the model has high prediction precision,which can realize real-time trace and prediction and has definite value of both theory and practice.