非结构化数据驱动的混合二次分解汇率区间多尺度组合预测

doi:10.16381/j.cnki.issn1003-207x.2021.0194

中国管理科学 ›› 2023, Vol. 31 ›› Issue (6): 60-70.doi: 10.16381/j.cnki.issn1003-207x.2021.0194

• 论文 • 上一篇

非结构化数据驱动的混合二次分解汇率区间多尺度组合预测

刘金培¹, 罗瑞², 陈华友², 陶志富²

1.安徽大学商学院，安徽合肥230601； 2.安徽大学大数据与统计学院，安徽合肥230601

收稿日期:2021-01-27 修回日期:2021-05-24 发布日期:2023-06-17
通讯作者: 陈华友(1969-)，男(汉族)，安徽和县人，安徽大学大数据与统计学院，教授，博士，研究方向：预测与决策分析，Email:huayouc@126.com. E-mail:huayouc@126.com
基金资助:
国家自然科学基金资助项目(72071001,72271002,72001001,71871001);教育部人文社会科学研究规划基金资助项目(20YJAZH066,21YJCZH148);安徽省自然科学基金资助项目(2008085MG226,1908085J03);安徽省高校优秀青年人才支持计划重点项目(gxyqZD2022001)

Multi-scale Combination Forecasting of Interval Exchange Rate with Hybrid Secondary Decomposition Driven by Unstructured Data

LIU Jin-pei¹, LUO Rui², CHEN Hua-you², TAO Zhi-fu²

1. School of Business, Anhui University, Hefei 230601, China;2. School of Big Data and Statistics, Anhui University, Hefei 230601, China

Received:2021-01-27 Revised:2021-05-24 Published:2023-06-17
Contact: 陈华友 E-mail:huayouc@126.com

摘要/Abstract

摘要： 汇率的变化具有非线性、非平稳、连续变化等特点，传统离散点值预测会损失其波动信息，也无法表示和估计其波动的区间范围。考虑到海量的互联网非结构化数据为汇率预测提供了大量的有效信息，本文提出一种非结构化数据驱动的混合二次分解汇率区间多尺度组合预测方法。首先，从百度指数中提取汇率相关的非结构化数据，利用奇异谱分析(SSA)对其进行去噪，并通过主成分分析(PCA)实现非结构数据的降维。其次，为了充分提取非结构数据和汇率区间序列的有效信息，基于集成经验模态分解(EEMD)、变模态分解(VMD)、奇异谱分析(SSA)和小波变换(WT)对汇率区间序列进行混合二次分解，得到多个不同分解路径下的汇率区间分解结果。然后，针对汇率区间分解得到的高频、低频和趋势序列的特征，基于非结构化数据，建立长短期记忆模型(LSTM)、支持向量回归(SVR)和BP神经网络(BPNN)三种单项预测方法分别对其进行预测，并对结果进行集成，得到不同分解路径下的汇率区间预测值。最后，利用最优加权组合方法对不同分解路径下的汇率区间预测值进行组合，得到汇率区间的最终预测结果。为验证该组合预测方法的有效性，本文对2018-2020年的美元兑人民币日汇率区间时间序列进行实证预测分析，结果表明，本文方法适用于具有高噪声的汇率区间预测，与已有方法相比具有更高的精确度和良好的适用性。

关键词: 区间组合预测；汇率；非结构化数据；混合二次分解；长短记忆模型(LSTM)

Abstract: The foreign exchange rate has the characteristics of non-linear, non-stationary, continuous change, etc. The traditional forecasting method of point value will lose its fluctuation information. In addition, the single data decomposition method has its inherent defects, there is the problem of incomplete decomposition, and the choice of decomposition method is uncertain. At the same time, massive unstructured data on the Internet provide a large amount of effective information for exchange rate prediction, but there is still a lack of systematic research on how to use unstructured data for exchange rate interval prediction.

Key words: interval combination prediction; exchange rate; unstructured data; mixed secondary decomposition; LSTM

中图分类号:

C934

刘金培, 罗瑞, 陈华友, 陶志富. 非结构化数据驱动的混合二次分解汇率区间多尺度组合预测[J]. 中国管理科学, 2023, 31(6): 60-70.

LIU Jin-pei, LUO Rui, CHEN Hua-you, TAO Zhi-fu. Multi-scale Combination Forecasting of Interval Exchange Rate with Hybrid Secondary Decomposition Driven by Unstructured Data[J]. Chinese Journal of Management Science, 2023, 31(6): 60-70.

参考文献

［1］ Colombo E, Pelagatti M. Statistical learning and exchange rate forecasting［J］. International Journal of Forecasting, 2020, 36(4): 1260-1289.
［2］ Sun Shaolong, Wang Shouyang, Wei Yunjie. Interval forecasting of exchange rates: a new interval decomposition ensemble approach［J］. Industrial Management & Data Systems, 2020, DOI:10.1108/IMDS-03-2019-0194.
［3］ Carriero A, Kapetanios G, Marcellino M. Forecasting exchange rates with a large Bayesian VAR［J］. International Journal of Forecasting, 2009, 25(2): 400-417.
［4］ Xiong Tao, Li Chongguang, Bao Yukun. Interval-valued time series forecasting using a novel hybrid HoltI and MSVR model［J］. Economic Modelling, 2017, 60: 11-23.
［5］ Sun Shaolong, Wang Shouyang, Wei Yunjie. A new multiscale decomposition ensemble approach for forecasting exchange rates［J］. Economic Modelling, 2019, 81: 49-58.
［6］ Galeshchuk S. Neural networks performance in exchange rate prediction［J］. Neurocomputing, 2016, 172: 446-452.
［7］ Kuan Chungming, Liu Tung. Forecasting exchange rates using feedforward and recurrent neural networks［J］. Journal of Applied Econometrics, 1995, 10(4): 347-364.
［8］肖进, 孙海燕, 刘敦虎. 基于GMDH混合模型的能源消费量预测研究［J］. 中国管理科学, 2017, 25(12): 158-166.Xiao Jin, Sun Haiyan, Liu Dunhu. GMDH based hybrid model for China's energy consumption prediction［J］. Chinese Journal of Management Science, 2017, 25(12): 158-166.
［9］ Wu Yungao, Gao Jianwei. Application of support vector neural network with variational mode decomposition for exchange rate forecasting［J］. Soft Computing, 2019, 23(16): 6995-7004.
［10］ Wang Yingnan, Du Jiangze, Jiang Chonghui, et al. Chinese currency exchange rates forecasting with EMD-based neural network［J］. Complexity, 2019, 2019: 7458961.
［11］ Lin Hualing, Sun Qiubi, Chen Shengqun. Reducing exchange rate risks in international trade: a hybrid forecasting approach of CEEMDAN and multilayer LSTM［J］. Sustainability, 2020, 12(6): 2451.
［12］ Sun Wei, Huang Chenchen. A hybrid air pollutant concentration prediction model combining secondary decomposition and sequence reconstruction［J］. Environmental Pollution, 2020, 266(3): 115216.
［13］ Sun Wei, Huang Chenchen. A novel carbon price prediction model combines the secondary decomposition algorithm and the Long Short-term Memory Network［J］. Energy, 2020, 207: 118294.
［14］刘金培, 郭艺, 陈华友. 基于非结构数据流行学习的碳价格多尺度组合预测［J］. 控制与决策, 2019, 34(2): 279-286.Liu Jinpei, Guo Yi, Chen Huayou. Multi-scale combined forecast of carbon price based on manifold learning of unstructured data［J］. Control and Decision, 2019, 34(2): 279-286.
［15］ Sun Shaolong, Wei Yunjie, Tsui K L, et al. Forecasting tourist arrivals with machine learning and internet search index［J］. Tourism Management, 2019, 70: 1-10.
［16］ Wang Jue, Athanasopoulos G, Hyndman R J, et al. Crude oil price forecasting based on internet concern using an extreme learning machine［J］. International Journal of Forecasting, 2018, 34(4): 665-677.
［17］ Li Shuqi, Liu Xiaoming, Lin Aijing. Paper fractional frequency hybrid model based on EEMD for financial time series forecasting［J］. Communications in Nonlinear Science and Numerical Simulation, 2020, 89: 105281.
［18］ Dragomiretskiy K, Zosso D. Variational mode decomposition［J］. IEEE Transactions on Signal Processing, 2014, 62(3): 531-544.
［19］ Zhang Yagang, Chen Bing, Pan Guifang, et al. A novel hybrid model based on VMD-WT and PCA-BP-RBF neural network for short-term wind speed forecasting［J］. Energy Conversion and Management, 2019, 195: 180-197.
［20］梁小珍, 郭战坤, 张倩文. 基于奇异谱分析的航空客运需求分析与分解集成预测模型［J］. 系统工程理论与实践, 2020, 40(7): 1844-1855.Liang Xiaozhen, Guo Zhankun, Zhang Qianwen. An analysis and decomposition ensemble prediction model for air passenger demand based on singular spectrum analysis［J］. Systems Engineering-Theory & Practice, 2020, 40(7): 1844-1855.
［21］欧阳红兵, 黄亢, 闫洪举. 基于LSTM神经网络的金融时间序列预测［J］. 中国管理科学, 2020, 28(4): 27-35.Ouyang Hongbing, Huang Kang, Yan Hongju. Prediction of financial time series based on LSTM neural network［J］. Chinese Journal of Management Science, 2020, 28(4): 27-35.
［22］ Kong Weicong, Dong Zhaoyang, Jia Youwei, et al. Short-term residential load forecasting based on LSTM recurrent neural network［J］. IEEE Transactionson Smart Grid, 2019, 10(1): 841-851.
［23］ Garcia-Ascanio C, Mate C. Electric power demand forecasting using interval time series: a comparison between VAR and iMLP［J］. Energy Policy, 2010, 38(2): 715-725.

[1]	聂福海, 李电生, 肖婷婷. 店中店模式对资金约束供应链生产和销售努力决策的影响研究[J]. 中国管理科学, 2023, 31(5): 141-151.
[2]	刘靓晨, 翟昕. 面对策略型消费者的企业最优创新与定价策略[J]. 中国管理科学, 2023, 31(4): 56-65.
[3]	韩梦圆, 冯良清, 张蕾. 绿色供应链中三重竞争对可替代产品的影响[J]. 中国管理科学, 2023, 31(4): 89-100.
[4]	李涛, 刘斌. 不同权力结构下竞争性供应链合作广告策略研究[J]. 中国管理科学, 2023, 31(4): 121-129.
[5]	朱峰, 刘玉敏, 徐济超, 苏冰杰. 一种考虑形状—位置概率语言术语相似度及应用[J]. 中国管理科学, 2023, 31(4): 250-259.
[6]	丁天蓉, 张源凯, 王玉英, 胡祥培. 基于关联网络的“人-机”双拣选系统仓储商品选择方法[J]. 中国管理科学, 2023, 31(3): 26-37.
[7]	李建斌, 王莹莹, 李明康, 郑宇婷. 医药电商考虑信息动态更新和最小化拆单率的订单重分配优化研究[J]. 中国管理科学, 2023, 31(3): 38-47.
[8]	侯芳,. 具有前置反馈特征的社群化制造网络评价方法[J]. 中国管理科学, 2023, 31(3): 238-250.
[9]	张琳, 田军. 协议企业应急物资生产与原材料预储决策研究[J]. 中国管理科学, 2023, 31(2): 108-117.
[10]	周建亨, 沈祎婷. 基于可持续性消费的企业质量价格决策研究[J]. 中国管理科学, 2023, 31(1): 9-20.
[11]	温锋华, 沈体雁, 邢江波, 寇晨欢. 城市突发公共卫生事件的循证治理机制研究[J]. 中国管理科学, 2023, 31(1): 206-215.
[12]	周德群, 周显扬, 丁浩. 多市场协同下的可再生能源电力竞价策略研究[J]. 中国管理科学, 2023, 31(1): 248-255.
[13]	陈希, 张怡斐, 孙亚亚, 梁海明, 张文博. 面向突发事件的应急献血者聚类与分配方法研究[J]. 中国管理科学, 2022, 30(12): 77-85.
[14]	焦媛媛, 闫鑫, 杜军, Roger Jiao. 社交网络情境下基于同侪影响的产品线规划研究[J]. 中国管理科学, 2022, 30(12): 108-119.
[15]	牛奔, 郭晨, 唐恒. 基于多目标多元学习细菌觅食优化算法的混合数据聚类[J]. 中国管理科学, 2022, 30(12): 131-140.

非结构化数据驱动的混合二次分解汇率区间多尺度组合预测

Multi-scale Combination Forecasting of Interval Exchange Rate with Hybrid Secondary Decomposition Driven by Unstructured Data

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 0