摘要
针对传统的时间序列线性预测算法对时间序列的线性程度要求高,而非线性方法一般建模复杂且计算量大,提出了一种基于趋势点状态模型的时间序列预测算法。该算法无须考虑时间序列是否具有显著线性特征,通过序列间耦合度挖掘时间序列上的相似子序列,找出相对应的相似序列趋势点,建立趋势点状态模型并求出预测值。算法建模简单,复杂度较低。通过模拟实验,结果表明该算法性能良好,尤其对具有周期性的时间序列预测精度很高。
The traditional linear time series prediction algorithms for time series require high linearity, and nonlinear methods are generally modeling complex and have a large computation. For the above, this paper proposed an algorithm for time series prediction which based on trends point state model. The algorithm didn＇ t regard to whether the time series forecast significant linear features, first digged out the similar sequence on time series through the coupling between sequences, and identified the corresponding trend points of similar sequence, then established the trend point state model and calculated the predicted value. Using this algorithm modeling is simple, arid the complexity is low. Through simulation, the results show that the algorithm has a high prediction accuracy, especially for periodic time series.
出处
《计算机应用研究》
CSCD
北大核心
2011年第12期4510-4512,4516,共4页
Application Research of Computers
关键词
时间序列
相似序列
趋势点状态模型
预测
周期
time series
similar sequence
trends point state model（TPSM）
prediction
periodic
