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| 面向限价指令簿趋势分析的网络集成模型 | |
| 引用本文： | 吕雪瑞,张莉.面向限价指令簿趋势分析的网络集成模型[J].模式识别与人工智能,2021,34(8):751-759. |
| 作者姓名： | 吕雪瑞 张莉 |
| 作者单位： | 1.苏州大学 计算机科学与技术学院 苏州 215006 2.苏州大学 机器学习与类脑计算国际合作联合实验室 苏州 215006 |
| 基金项目： | 江苏省高校自然科学研究项目(No.19KJA550002)、江苏省六大人才高峰项目(No.XYDXX-054)、江苏高校优势学科建设工程项目资助 |
| 摘 要： | 为了更好地分析限价指令簿(LOBs)的趋势,文中提出面向LOBs趋势分析的网络集成模型(NEM-LOB).模型融合2个长短期记忆(LSTM)子模型和1个卷积神经网络(CNN)子模型.一个LSTM子模型可通过LOBs的分布信息捕捉全局时间依赖性,另一个LSTM子模型可通过LOBs和订单流的动态信息捕捉全局动态性.CNN子模型通过LOBs的事实信息提取局部特征.最后,结合3个子模型,提取特征以获得预测结果.在FI-2010数据集上的实验表明NEM-LOB通过引入订单流信息,能对LOBs进行更好的趋势分析. |
| 关 键 词： | 订单流 限价指令簿(LOBs) 集成模型 卷积神经网络(CNN) 长短期记忆(LSTM) |
| 收稿时间： | 2021-04-28 |
Network Ensemble Model for Trend Analysis of Limit Order Books |
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| LÜ,Xuerui,ZHANG Li.Network Ensemble Model for Trend Analysis of Limit Order Books[J].Pattern Recognition and Artificial Intelligence,2021,34(8):751-759. | |
| Authors: | LÜ Xuerui ZHANG Li |
| Affiliation: | 1. School of Computer Science and Technology, Soochow University, Suzhou 215006 2. Joint International Research Laboratory of Machine Learning and Neuromorphic Computing, Soochow University, Suzhou 215006 |
| Abstract: | To analyze the trend of limit order books(LOBs) better, a network ensemble model for trend analysis of LOBs(NEM-LOB) is proposed. Two long short-term memory(LSTM) sub-models and one convolutional neural network sub-model are integrated in NEM-LOB. One LSTM sub-model captures the global temporal dependence through the distribution information of LOBs. The other LSTM sub-model captures the global dynamics through the dynamic information of LOBs and order streams. The local features are extracted through the factual information of LOBs. Finally, three sub-models are combined to extract features to obtain prediction results. Experiments on FI-2010 dataset show that NEM-LOB makes a better trend analysis for LOBs by combining order streams. |
| Keywords: | Order Streams Limit Order Books(LOBs) Ensemble Model Convolutional Neural Network(CNN) Long Short-Term Memory(LSTM) |
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