ZHANG Qiaoling, GAO Shuping, HE Di, CHENG Mengfei. A Hybrid Neural Network Data Fusion Algorithm Based on Time Series[J]. Applied Mathematics and Mechanics, 2021, 42(1): 82-91. doi: 10.21656/1000-0887.410056
Citation: ZHANG Qiaoling, GAO Shuping, HE Di, CHENG Mengfei. A Hybrid Neural Network Data Fusion Algorithm Based on Time Series[J]. Applied Mathematics and Mechanics, 2021, 42(1): 82-91. doi: 10.21656/1000-0887.410056

A Hybrid Neural Network Data Fusion Algorithm Based on Time Series

doi: 10.21656/1000-0887.410056
Funds:  The National Natural Science Foundation of China(91338115)
  • Received Date: 2020-02-19
  • Rev Recd Date: 2020-06-27
  • Publish Date: 2021-01-01
  • For traditional data fusion algorithms, the fusion performance of high-noise, large-scale and complex-structure time series data is poor. A hybrid neural network data fusion algorithm (i.e. the SCLG algorithm) was proposed to solve this problem. Firstly, the time series data were decomposed and reconstructed with the singular spectrum analysis algorithm to eliminate noise. Secondly, the spatial and short-term characteristics of the data were extracted by means of the deep convolutional neural network. Thirdly, the long short-term memory neural network and the gated recurrent unit neural network were introduced to extract data features in the time dimension. Finally, the fully connected layer was applied to integrate the main information and output the final decision. The experimental results from the SP&500 and AQI data sets show that, the proposed algorithm is superior to DCNN, CNN-LSTM and FDL in terms of fusion performance and stability.
  • [1]
    孙友强. 时间序列数据挖掘中的维数约简与预测方法研究[D]. 博士学位论文. 合肥: 中国科学技术大学, 2014.(SUN Youqiang. Research on dimensionality reduction and prediction methods in time series data ming[D]. PhD Thesis. Hefei: University of Science and Technology of China, 2014.(in Chinese))
    [2]
    王晓锋. 时间序列数据挖掘在医疗领域的应用[J]. 软件导刊, 2011,10(5): 123-124.(WANG Xiaofeng. Application of time series data mining in the medical field[J]. Software Guide,2011,10(5): 123-124.(in Chinese))
    [3]
    黄超, 龚惠群. 金融领域时间序列挖掘技术研究[J]. 东南大学学报(哲学社会科学版), 2007,9(5): 36-39.(HUANG Chao, GONG Huiqun. A study of time series mining technology in financial field[J]. Journal of Southeast University(Philosophy and Social Science),2007,9(5): 36-39.(in Chinese))
    [4]
    彭跃华, 于江龙. 非线性时间序列分析在气候中的应用研究进展[J]. 气象, 2009,32(10): 3-7.(PENG Yuehua, YU Jianglong. Research advances of nonlinear time series analysis applying in climatology[J]. Meteorological Monthly,2009,32(10): 3-7.(in Chinese))
    [5]
    孟小峰, 杜志娟. 大数据融合研究: 问题与挑战[J]. 计算机研究与发展, 2016,53(2): 231-246.(MENG Xiaofeng, DU Zhijuan. Research on the big data fusion: issues and challenges[J]. Journal of Computer Research and Development,2016,53(2): 231-246.(in Chinese))
    [6]
    何绪飞, 艾剑良, 宋智桃. 多元数据融合在无人机结构健康监测中的应用[J]. 应用数学和力学, 2018,39(4): 395-402.(HE Xufei, AI Jianliang, SONG Zhitao. Multi-Source data fusion for health monitoring of unmanned aerial vehicle structures[J]. Applied Mathematics and Mechanics,2018,39(4): 395-402.(in Chinese))
    [7]
    LECUN Y, BENGIO Y, HINTON G. Deeplearning[J]. Nature,2015,521: 436-444.
    [8]
    SHEN F, CHAO J, ZHAO J. Forecasting exchange rate using deep belief networks and conjugate gradient method[J]. Neurocomputing,2015,167: 243-253.
    [9]
    KIM T Y, CHO S B. Predicting residential energy consumption using CNN-LSTM neural networks[J]. Energy,2019,182: 72-81.
    [10]
    MA Q L, TIAN S, WEI J, et al. Attention-based spatio-temporal dependence learning network[J]. Information Sciences,2019,503: 92-108.
    [11]
    KARIM F, MAJUMDAR S, DARABI H, et al. Multivariate LSTM-FCNs for time series classification[J]. Neural Networks,2019,116: 237-245.
    [12]
    KARIM F, MAJUMDAR S, DARABI H, et al. LSTM fully convolutional networks for time series classification[J]. IEEE Access,2017,6(99): 1662-1669.
    [13]
    HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 2018: 7132-7141.
    [14]
    JING L, WANG T, ZHAO M, et al. An adaptive multi-sensor data fusion method based on deep convolutional neural networks for fault diagnosis of planetary gearbox[J]. Sensors,2017,17(3): 414. DOI: 10.3390/s17020414.
    [15]
    GU Y L, LU W Q, QIN L Q, et al. Short-term prediction of lane-level traffic speeds: a fusion deep learning model[J]. Transportation Research Part C: Emerging Technologies,2019,106: 1-16.
    [16]
    HASSANI H, MAHMOUDVAND R, ZOKAEI M, et al. On the separability between signal and noise in singular spectrum analysis[J]. Fluctuation and Noise Letters,2012,11(2): 1250014.
    [17]
    ZHIGLJAVSKY A. Singular spectrum analysis for time series: introduction to this special issue[J]. Statistics and Its Interface,2010,3(3): 255-258.
    [18]
    俞颂华. 卷积神经网络的发展与应用综述[J]. 信息通信, 2019(2): 39-43.(YU Songhua. The development and application of convolutional neural networks[J]. Information & Communications,2019(2): 39-43.(in Chinese))
    [19]
    MALEK S, MELGANI F, BAZI Y. One-dimensional convolutional neural networks for spectroscopic signal regression[J]. Journal of Chemometrics,2018,32(5): e2977. DOI: 10.1002/cem.2977.
    [20]
    HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation,1997,9(8): 1735-1780.
    [21]
    HUANG C J, KUO P H. A deep CNN-LSTM model for particulate matter (PM2.5) forecasting in smart cities[J]. Sensors,2018,18(7): 2220.
    [22]
    SAGHEER A, KOTB M. Time series forecasting of petroleum production using deep LSTM recurrent networks[J]. Neurocomputing,2019,323: 203-213.
    [23]
    ABDEL-HAMID O, DENG L, YU D. Exploring convolutional neural network structures and optimization techniques for speech recognition[C]//Interspeech 2013. Lyon, France, 2013: 1173-1175.
    [24]
    WEI B, JUN Y, RAO Y L, et al. A deep learning framework for financial time series using stacked autoencoders and long-short term memory[J].Plos One,2017, 12(7): 1-24.
  • Cited by

    Periodical cited type(13)

    1. 孙洁. 基于模糊数学理论的双通道数据关联融合算法. 吉林大学学报(信息科学版). 2025(01): 150-155 .
    2. 马乃轩,付文博,杨少华,齐麟,武略. 基于FSOM神经网络的桥梁监测数据缺失重构算法. 电子设计工程. 2024(04): 56-60 .
    3. 杨国俊,田里,唐光武,毛建博,杜永峰. D-S理论和Markov链组合的桥梁性能退化预测研究. 应用数学和力学. 2024(04): 416-428 . 本站查看
    4. 李鑫,梁永玲. 基于模糊数学的多源异构数据融合模型. 吉林大学学报(理学版). 2024(03): 691-696 .
    5. 陈家乐,张芸芸,崔红伟. 基于LSTM神经网络的办公建筑逐日能耗预测研究. 建筑节能(中英文). 2024(09): 74-78 .
    6. 苗俊田,鹿德台,李卓军,刘冬冬,赵博. 融合GRU单元的CNN网络在石油旋转机械故障诊断中的应用. 信息技术. 2024(10): 7-13 .
    7. 张文健. 基于数学技术和信息技术的数据融合方法. 广州城市职业学院学报. 2023(02): 92-95+100 .
    8. 龚玉晓,高淑萍. 基于改进深度残差收缩网络的心电信号分类算法. 应用数学和力学. 2023(08): 977-988 . 本站查看
    9. 郭蕾. 基于深度学习的运动员训练量监测信息整合. 信息技术. 2023(11): 143-147 .
    10. 王俊宇,邢国栋,李海涛,付革民. 结合离线计算与迁移学习的机务大数据闭环整合算法. 微型电脑应用. 2023(12): 101-104 .
    11. 王校昌,王大鹏. 结构动态监测信号缺失数据的LSTM和ARIMA预测对比研究. 苏州科技大学学报(工程技术版). 2022(04): 25-30 .
    12. 周济民,张海晨,王沫然. 基于物理经验模型约束的机器学习方法在页岩油产量预测中的应用. 应用数学和力学. 2021(09): 881-890 . 本站查看
    13. 李凌,崔强,陈曦,熊汉武,沈维捷,王琪. 面向电工装备智慧物联的边缘计算技术研究与应用. 自动化与仪器仪表. 2021(11): 45-51 .

    Other cited types(11)

  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (1993) PDF downloads(279) Cited by(24)
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return