Research on time series prediction algorithm based on gaussian process mixture model

LIU Ya-bo; WU Qiu-xuan

Chinese

您当前的位置：

首页 >

文章列表页 >

Research on time series prediction algorithm based on gaussian process mixture model

更新时间：2026-09-04

- Research on time series prediction algorithm based on gaussian process mixture model
- Vol. 38, Issue 6, Pages: 93-98(2021)
- 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：
- 基金信息：
- DOI：
  CLC：
- Published：2021
- 稿件说明：
移动端阅览
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.

关键词

Keywords

references

许美玲, 王依雯. 基于改进差分进化和回声状态网络的时间序列预测研究[J/OL]. 自动化学报, 1-10[2021-03-16]. https: //DOI.org/j.aas.c180549.DOI: 10.16383/j.aas.c180549. XU M L, WANG Y W. Time series prediction based on improved differential evolution and echo state network[J]. Acta Automatica Sinica, 1-10[2021-03-16]. https: //DOI.org/j.aas.c180549.DOI: 10.16383/j.aas.c180549.

LI L L, SUN J, WANG C H, et al. Enhanced Gaussian process mixture model for short-term electric load forecasting[J]. Information Sciences, 2019, 477: 386-398. DOI: 386

周亚同, 陈子一, 马尽文. 从高斯过程到高斯过程混合模型: 研究与展望[J]. 信号处理, 2016, 32(8): 960-972. DOI: 10.16798/j.issn.1003-0530.2016.08.11. ZHOU Y T, CHEN Z Y, MA J W. From Gaussian processes to the mixture of Gaussian processes: a survey[J]. Journal of Signal Processing, 2016, 32(8): 960-972. DOI: 10.16798/j.issn.1003-0530.2016.08.11.

彭虹桥, 顾洁, 胡玉, 等. 基于混沌粒子群-高斯过程回归的饱和负荷概率预测模型[J]. 电力系统自动化, 2017, 41(21): 25-32. DOI: 10.7500/AEPS20170119007. PENG H Q, GU J, HU Y, et al. Forecasting model for saturated load based on chaotic particle swarm optimization-Gaussian process regression[J]. Automation of Electric Power Systems, 2017, 41(21): 25-32. DOI: 10.7500/AEPS20170119007.

王鑫, 吴际, 刘超, 等. 基于LSTM循环神经网络的故障时间序列预测[J]. 北京航空航天大学学报, 2018, 44(4): 772-784. DOI: 10.13700/j.bh.1001-5965.2017.0285. WANG X, WU J, LIU C, et al. Exploring LSTM based recurrent neural network for failure time series prediction[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(4): 772-784. DOI: 10.13700/j.bh.1001-5965.2017.0285.

孙斌, 姚海涛, 刘婷. 基于高斯过程回归的短期风速预测[J]. 中国电机工程学报, 2012, 32(29): 104-109. DOI: 10.13334/j.0258-8013.pcsee.2012.29.016. SUN B, YAO H T, LIU T. Short-term wind speed forecasting based on Gaussian process regression model[J]. Proceedings of the CSEE, 2012, 32(29): 104-109. DOI: 10.13334/j.0258-8013.pcsee.2012.29.016.

JAIPURIA S, MAHAPATRA S S. An improved demand forecasting method to reduce bullwhip effect in supply chains[J]. Expert Systems with Applications, 2014, 41(5): 2395-2408. DOI: 10.1016/j.eswa.2013.09.038.

ARUNRAJ N S, AHRENS D, FERNANDES M. Application of SARIMAX model to forecast daily sales in food retail industry[J]. International Journal of Operations Research and Information Systems, 20 16, 7(2): 1-2 1. DOI: 10.40 18/IJORIS.20 16040 10 1.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Steganography model guided by attention mechanism and improved dense convolution

Aerial vehicle detection algorithm based on multi-scale fusion and small-object enhancement

Monte carlo analysis of V-layout trench SiC neutron detector

Implementation and verification of a dual-chain power gating strategy for low power design

A review of large model development: technological evolution from BERT to Deepseek

Related Author

No data

Related Institution

No data

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰