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基于EWT-SSA-PSO-ELM模型的P2P网贷市场收益率预测
Prediction of P2P online lending market yield based on EWT-SSA-PSO-ELM model
摘要点击 428  全文点击 0  投稿时间:2018-11-01  修订日期:2019-12-18
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中文关键词  P2P网贷市场收益率;EWT分解算法;SSA分解算法;PSO-ELM模型
英文关键词  peer to peer online lending market yield; empirical wavelet transform algorithm; singular spectrum analysis algorithm; PSO-ELM model
基金项目  国家自然科学基金项目(71573042), 福建省自然科学基金项目(2017J01794)
作者单位邮编
崔金鑫 福州大学经济与管理学院 350116
邹辉文 福州大学经济与管理学院 350116
中文摘要
      鉴于目前鲜有研究关注P2P网贷市场收益率预测问题, 针对已有金融市场收益率预测研究存在的不足, 文章提出了一种基于两阶段分解技术和粒子群优化极限学习机的EWT-SSA-PSO-ELM预测模型. 引入EWT经验小波分 解算法对原始的收益率综指序列进行分解, 进而提高原始序列的分解效率; 采用Lempel-Ziv复杂度算法提升模式分量重构的科学性, 避免以往分量重构过程的随意性; 利用SSA奇异谱分解算法对高频重构分量进行降噪, 从而提升 高频重构分量预测效果. 基于该预测模型对P2P网贷市场收益率综指进行预测, 实证结果表明: 文章所构建的收益率预测模型的性能显著优于其余基准对比模型.
英文摘要
      Given that there are few studies focusing on the prediction of P2P online lending market yield and there are many shortcomings in the existing yield prediction studies, this paper proposes a novel hybrid EWT-SSA-PSO-ELM forecasting model based on two-phase decomposition technique and Extreme Learning Machine optimized by Particle Swarm Optimization. Firstly, this paper introduces the novel EmpiricalWavelet Transform algorithm to improve the decomposition efficiency of the original yield composite index series. Meanwhile, this paper utilizes the Lempel-Ziv complexity algorithm to enhance the reconstruction scientificity of the empirical modes, avoiding the randomness of the previous empirical modes reconstruction methods. Thirdly, this paper uses the Singular Spectrum Analysis algorithm to perform noise reduction on the highfrequency component, further improving the prediction performance. The experimental results demonstrate that the proposed EWT-SSA-PSO-ELM model has superior prediction performance compared with each benchmark forecasting model.
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