BWCG和NGARCH的自适应支持向量回归调整复合模型在时间序列预测中的应用

IF 1 4区 工程技术 Q4 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
H. Tsai, B. Chang
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引用次数: 2

摘要

灰色模型在应用于非周期短期预测时遇到了超调的关键问题。与此同时,累积3点最小二乘线性预测(C3LSP)交替面临相反的情况,即低估。然而,本文提出了一种结合上述两种模型的方法,即混合bpnn加权灰色- c3lsp预测(BWGC)模型,可以有效地解决超调和低估现象。然而,BWGC的一些预测结果不够准确,因为很少有观测值偏离GM和C3LSP的输出。因此,对BWGC中残差的时变方差进行了补偿。即将非线性广义自回归条件异方差(NGARCH)引入到BWGC中,然后利用自适应支持向量回归(ASVR)对BWGC和NGARCH调整合适的系数,有效提高预测精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive Support Vector Regression Tuning Composite Model of BWCG and NGARCH for Applications of Time-Series Prediction
Grey model (GM) has encountered the crucial problem of overshoot when applying to the non-periodic short-term prediction. At the same period, cumulated 3-point least squared linear prediction (C3LSP) alternatively confronts the opposite situation, i.e. underestimation. Nevertheless, a method of combining both preceding models is proposed for resolving the overshoot and underestimation phenomena significantly that is hybrid BPNN-weighted GREY-C3LSP prediction (BWGC) model. However, some predicted outcomes resulted from BWGC are not accurate enough as few observations deviate far away from both GM and C3LSP outputs. Thus, compensation is figured out to deal with the time-varying variance of the residuals in BWGC. That is, incorporating a non-linear generalized autoregressive conditional heteroscedasticity (NGARCH) into BWGC is applied, and then adaptive support vector regression (ASVR) is employed for tuning the appropriate coefficients for both BWGC and NGARCH to effectively improve the predictive accuracy.
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来源期刊
Journal of Grey System
Journal of Grey System 数学-数学跨学科应用
CiteScore
2.40
自引率
43.80%
发文量
0
审稿时长
1.5 months
期刊介绍: The journal is a forum of the highest professional quality for both scientists and practitioners to exchange ideas and publish new discoveries on a vast array of topics and issues in grey system. It aims to bring forth anything from either innovative to known theories or practical applications in grey system. It provides everyone opportunities to present, criticize, and discuss their findings and ideas with others. A number of areas of particular interest (but not limited) are listed as follows: Grey mathematics- Generator of Grey Sequences- Grey Incidence Analysis Models- Grey Clustering Evaluation Models- Grey Prediction Models- Grey Decision Making Models- Grey Programming Models- Grey Input and Output Models- Grey Control- Grey Game- Practical Applications.
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