基于高斯过程混合模型的时间序列预测算法研究

刘亚波; 吴秋轩

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基于高斯过程混合模型的时间序列预测算法研究

更新时间：2026-09-04

- 基于高斯过程混合模型的时间序列预测算法研究
- Research on time series prediction algorithm based on gaussian process mixture model
- 微电子学与计算机 2021年38卷第6期页码：93-98
- 作者机构：
  
  杭州电子科技大学机构自动化学院,浙江,杭州,310000
- 作者简介：
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- 纸质出版：2021
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刘亚波, 吴秋轩. 基于高斯过程混合模型的时间序列预测算法研究[J]. 微电子学与计算机, 2021, 38(6): 93-98.

LIU Ya-bo, WU Qiu-xuan. Research on time series prediction algorithm based on gaussian process mixture model[J]. 2021, 38(6): 93-98.
刘亚波, 吴秋轩. 基于高斯过程混合模型的时间序列预测算法研究[J]. 微电子学与计算机, 2021, 38(6): 93-98. DOI：

LIU Ya-bo, WU Qiu-xuan. Research on time series prediction algorithm based on gaussian process mixture model[J]. 2021, 38(6): 93-98. DOI：

摘要

针对实时变化且不同时段差异大的时间序列，提出一种基于高斯过程混合模型的预测算法.该算法首先对时间序列进行预处理，并采用密度空间含噪聚类(DBSCAN)去除奇点.然后针对扩展迪基-福勒(ADF)检验结果将时间序列分为常数项、平稳和非平稳三类，最后基于高斯过程混合(GPM)模型对各类时间序列进行预测，并和差分自回归移动平均模型(ARIMA)、支持向量机(SVM)、高斯过程(GP)模型进行性能对比.以采购商品报价时间序列为例进行的预测结果表明：GP模型与GPM模型均能输出预测置信区间，给出预测结果的可信程度；GPM模型的优势是能够更精准刻画时间序列各时段差异，预测精度更高.

Abstract

Aiming at the time series that change in real time and differ greatly in different time periods

a prediction algorithm based on Gaussian process mixture model is proposed. Firstly

the time series is preprocessed and the singularities are removed by density-space noise-containing clustering (DBSCAN). Then

according to the results of the extended Diki-Fowler test

the time series are divided into three categories: constant term

stationary term and non-stationary term. Finally

the prediction of various time series is made based on the Gaussian process mixture (GPM) model

and the performance of the different autoregressive moving average model (ARIMA)

support vector machine (SVM) and Gaussian process (GP) model is compared. Taking the time series of government procurement commodity quotes as an example

the results show that both the GP model and the GPM model can output prediction confidence intervals and give the credibility of the prediction results. The advantage of the GPM model is that it can more accurately characterize the differences of time series in different periods

and the prediction accuracy is higher.

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references

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