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时间序列分析模型在黄海南部小黄鱼资源量预测中的应用

宋大德 汪金涛 陈新军 仲霞铭 熊瑛 汤建华 吴磊

宋大德,汪金涛,陈新军,等. 时间序列分析模型在黄海南部小黄鱼资源量预测中的应用[J]. 海洋学报,2020,42(12):26–33 doi: 10.3969/j.issn.0253-4193.2020.12.003
引用本文: 宋大德,汪金涛,陈新军,等. 时间序列分析模型在黄海南部小黄鱼资源量预测中的应用[J]. 海洋学报,2020,42(12):26–33 doi: 10.3969/j.issn.0253-4193.2020.12.003
Song Dade,Wang Jintao,Chen Xinjun, et al. Application of time series analysis model on stock prediction of small yellow croaker ( Larimichthys polyactis) in the southern Yellow Sea[J]. Haiyang Xuebao,2020, 42(12):26–33 doi: 10.3969/j.issn.0253-4193.2020.12.003
Citation: Song Dade,Wang Jintao,Chen Xinjun, et al. Application of time series analysis model on stock prediction of small yellow croaker ( Larimichthys polyactis ) in the southern Yellow Sea[J]. Haiyang Xuebao,2020, 42(12):26–33 doi: 10.3969/j.issn.0253-4193.2020.12.003

时间序列分析模型在黄海南部小黄鱼资源量预测中的应用

doi: 10.3969/j.issn.0253-4193.2020.12.003
基金项目: 国家自然科学基金(31802297);江苏省六大人才高峰项目(NY-031);农业农村部专项—东海区海洋渔业资源调查与监测研究专项;江苏省农业农村综合信息统计监测调查;江苏省水生野生动物普查专项(ZYHB16-2)。
详细信息
    作者简介:

    宋大德(1997-),男,江苏省徐州市人,主要从事渔业资源生物学研究。E-mail:1315822749@qq.com

    通讯作者:

    熊瑛,研究员,主要从事海洋渔业资源研究。E-mail:yxiongshfu@126.com

  • 中图分类号: S917.4

Application of time series analysis model on stock prediction of small yellow croaker (Larimichthys polyactis) in the southern Yellow Sea

  • 摘要: 本文选取2003−2014年黄海南部帆式张网小黄鱼渔获量的监测数据,运用时间序列分析模型ARIMA (1,2,0)进行拟合及预测,并用2015−2016年小黄鱼年单位捕捞努力量渔获量值进行验证。结果显示,2003−2014年的小黄鱼年单位捕捞努力量渔获量模拟值与真实值接近,相关系数为0.881 (p<0.05),相关性显著;2015年和2016年预测值分别为47.66 kg/网和49.16 kg/网,与实际值(51.10 kg/网和40.05 kg/网)相对误差分别为6.73%和22.75%,总体相对误差为14.74%。表明ARIMA (1,2,0)模型对黄海南部小黄鱼渔获量时间序列的变化趋势进行拟合和预测是可行的,在短期预测方面更具优势。不同时间序列数据ARIMA模型的pdq值不尽一致,在数据分析时不能简单地套用固定模型,应根据相关理论指导和分析,确定适宜的pdq值。
  • 图  1  2003−2016年黄海南部帆式张网调查区域所涉渔场

    Fig.  1  The fishing grounds surveyed by canvas stow nets in the southern Yellow Sea from 2003 to 2016

    图  2  二阶差分序列的自相关函数(ACF)和偏相关函数(PACF)图

    Fig.  2  The ACF and PACF diagrams of second order difference sequence

    图  3  ARIMA(1,2,0)模型残差序列自相关函数(ACF)和偏相关函数(PACF)图

    Fig.  3  The ACF and PACF diagrams of the ARIMA (1, 2, 0) model residual sequence

    图  4  2003−2014年黄海南部小黄鱼年CPUE模型拟合值与实际值关系

    Fig.  4  The relationship between simulated and actual CPUE of small yellow croaker in the southern Yellow Sea from 2003 to 2014

    表  1  2003−2016年黄海南部帆式张网监测船数、航次数和总投网次数

    Tab.  1  The number of monitoring vessel by canvas stow net, voyage and hauls from 2003 to 2016 in the southern Yellow Sea

    年份监测渔船数量/艘航次数/次总投网次数/网
    20032341 908
    20042251 897
    20052311 749
    20062301 722
    20072312 015
    20082322 202
    20092322 012
    20102352 005
    20112302 756
    20124342 268
    20132321 502
    20142323 035
    20153373 422
    20162362 892
    下载: 导出CSV

    表  2  4类模型的相关函数性质

    Tab.  2  The correlation function properties of four types of models

    模型系数AR (p)MA (q)ARMA (pq)ARIMA (pdq)
    ACF拖尾q阶截尾拖尾非平稳时间序列
    PACFp阶截尾拖尾拖尾
    下载: 导出CSV

    表  3  水平序列和差分序列单位根检验表

    Tab.  3  Unit root test of horizontal sequence and difference sequence

    数据处理单位根检验1%显著水平5%显著水平10%显著水平
    水平序列−2.766 2−4.200 1−3.175 4−2.729 0
    一阶差分序列−4.337 7−4.297 1−3.212 7−2.747 7
    二阶差分序列−6.318 4−4.420 6−3.259 8−2.771 1
    下载: 导出CSV

    表  4  ARIMA(1,2,0)模型参数估计表

    Tab.  4  The parameter estimation of the ARIMA (1, 2, 0) model

    模型参数估计值标准误
    ARIMA(1,2,0)AR1−1.0062.969
    常数项−0.7140.246
    下载: 导出CSV
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  • 收稿日期:  2020-03-25
  • 修回日期:  2020-07-01
  • 网络出版日期:  2021-01-06
  • 刊出日期:  2020-12-25

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