考虑协整的VECM-CoinSVR区间预测组合模型
VECM-CoinSVR hybrid model considering cointegration for interval-valued forecasting
查看参考文献24篇
|
文摘
|
为了提高区间预测的精度,提出一种考虑时间序列上下限协整关系的区间预测组合模型(VECM-CoinSVR).首先,用向量误差修正模型(VECM)捕获时间序列的线性成分,得到VECM的预测结果和预测残差序列;其次,通过协整检验获得残差序列上下限之间的协整向量,把该向量与残差序列的历史数据作为支持向量回归模型(SVR)的输入,得到Coin-SVR模型,并对残差序列进行预测;最后,将VECM的预测结果和残差序列Coin-SVR的预测结果相加得到区间组合预测结果.为了验证模型的有效性,将VECM-CoinSVR模型用于全国市场的牛肉、羊肉和活鸡价格的区间预测,与三个单模型(VECM,SVR, Coin-SVR)进行比较,在MAPE、MSEI和U~I三个预测精度指标上,VECM-CoinSVR组合模型的预测精度都明显提高.通过区间中心序列点预测结果的比较分析,进一步论证了区间预测优于点预测的观点. |
|
其他语种文摘
|
To improve the accuracy of interval forecasting, a VECM-CoinSVR hybrid model considering the cointegration between the upper and lower bounds for interval-valued forecasting is proposed.Vector error correction model(VECM) is firstly employed to fit the original time series so as to obtain the prediction .result and residual error series of VECM.Secondly, the cointegration vector between the upper and lower bounds of the residual error series is obtained by using cointegration test, then the cointegration vector and the historical data of residual error series are treated as the input of the support vector regression considered cointegration(Coin-SVR) to obtain the prediction result of the residual error series.Finally, the final prediction of VECM-CoinSVR is obtained by combining the prediction result of VECM and the prediction result of the residual error series.To verify the effectiveness of the proposed model, the interval forecast hybrid model is used for empirical research on the price forecasting of beef, mutton and live chicken in the national market.Compared with the three single models(VECM, SVR, Coin-SVR) and based on the criteria MAPE, MSEI, and U~I, VECM-CoinSVR has significantly higher prediction accuracy.By comparing with the point forecasting result of the interval center time series, the point that interval forecasting can yield a better result than point forecasting is further demonstrated. |
|
来源
|
系统工程理论与实践
,2021,41(11):3020-3030 【核心库】
|
|
DOI
|
10.12011/SETP2020-0996
|
|
关键词
|
区间预测
;
协整
;
价格预测
;
区间值时间序列
|
|
地址
|
华南农业大学数学与信息学院, 广州, 510642
|
|
语种
|
中文 |
|
文献类型
|
研究性论文 |
|
ISSN
|
1000-6788 |
|
学科
|
自动化技术、计算机技术 |
|
基金
|
国家自然科学基金
|
|
文献收藏号
|
CSCD:7090529
|
参考文献 共
24
共2页
|
|
1.
Wang X. Set-valued and interval-valued stationary time series.
Journal of Multivariate Analysis,2016(145):208-223
|
CSCD被引
4
次
|
|
|
|
|
2.
Sun Y Y. Threshold autoregressive models for interval-valued time series data.
Journal of Econometrics,2018,206(2):414-446
|
CSCD被引
10
次
|
|
|
|
|
3.
Zeng X Y. A novel multivariate grey model for forecasting the sequence of ternary interval numbers.
Applied Mathematical Modelling,2019(69):273-286
|
CSCD被引
5
次
|
|
|
|
|
4.
Leandro M. Interval fuzzy rule-based modeling approach for financial time series forecasting.
IEEE International Conference on Fuzzy Systems,2017
|
CSCD被引
1
次
|
|
|
|
|
5.
Arroyo J. Different approaches to forecast interval time series: A comparison in finance.
Computational Economics,2011,37(2):169-191
|
CSCD被引
6
次
|
|
|
|
|
6.
Chen Y Y. Time series interval forecast using GM (1,1) and NGBM (1, 1) models.
Soft Computing,2019(23):1541-1555
|
CSCD被引
1
次
|
|
|
|
|
7.
Rodrigues P M M. Modeling and forecasting interval time series with threshold models.
Advances in Data Analysis and Classification,2014,9(1):1-17
|
CSCD被引
5
次
|
|
|
|
|
8.
Xiong T. Interval forecasting of electricity demand: A novel bivariate EMD-based support vector regression modeling framework.
International Journal of Electrical Power & Energy Systems,2014(63):353-362
|
CSCD被引
6
次
|
|
|
|
|
9.
林鹏. 基于交叉验证方法的海洋经济发展的组合预测.
系统科学与数学,2018,38(7):823-829
|
CSCD被引
2
次
|
|
|
|
|
10.
张侃. 基于EGA算法的小样本非线性残差灰色Verhulst计量组合预测模型.
系统工程理论与实践,2017,37(10):2630-2639
|
CSCD被引
6
次
|
|
|
|
|
11.
Li G. The combination of interval forecasts in tourism.
Annals of Tourism Research,2019(75):363-378
|
CSCD被引
2
次
|
|
|
|
|
12.
Sun S L. Interval decomposition ensemble approach for crude oil price forecasting.
Energy Economics,2018(76):274-287
|
CSCD被引
4
次
|
|
|
|
|
13.
林焰. 基于模糊粒化的改进混合神经网络股指期货价格区间预测.
南方金融,2017(11):9-22
|
CSCD被引
1
次
|
|
|
|
|
14.
Erdogdu E. Electricity demand analysis using cointegration and ARIMA modelling: A case study of Turkey.
Energy Policy,2007,35(2):1129-1146
|
CSCD被引
10
次
|
|
|
|
|
15.
Reimers E. Interval forecasting in cointegrated systems.
Statistical Papers,1995,36(1):349-369
|
CSCD被引
1
次
|
|
|
|
|
16.
Wu J L. Probabilistic forecast of electricity price based on Adaboost_RBF method.
International Conference on Materials Engineering,2016:824-830
|
CSCD被引
1
次
|
|
|
|
|
17.
Wang Z C. An enhanced interval PM_(2.5) concentration forecasting model based on BEMD and MLP~I with influencing factors.
Atmospheric Environment,2019
|
CSCD被引
1
次
|
|
|
|
|
18.
Leandro M. A fuzzy inference system modeling approach for interval-valued symbolic data forecasting.
Knowledge-Based Systems,2019(164):139-149
|
CSCD被引
5
次
|
|
|
|
|
19.
Maulia E. Tax revenue and inflation rate predictions in Banda Aceh using vector error correction model (VECM).
IOP Conference Series: Materials Science and Engineering. 352,2018:012056
|
CSCD被引
1
次
|
|
|
|
|
20.
Zhu L Y. Do economic activities cause air pollution? Evidence from China's major cities.
Sustainable Cities and Society,2019,49:101593
|
CSCD被引
7
次
|
|
|
|
|
了解气象卫星更多信息，请访问