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Application of time series analysis model on stock prediction of small yellow croaker (Larimichthys polyactis) in the southern Yellow Sea
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Dade Song1, 2, Jintao Wang1, Xinjun Chen1, Xiaming Zhong2, Ying Xiong2, *, Jianhua Tang2, Lei Wu2
Haiyang Xuebao | 2020, 42(12) : 26 - 33
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Haiyang Xuebao | 2020, 42(12): 26-33
• Article •
Application of time series analysis model on stock prediction of small yellow croaker (Larimichthys polyactis) in the southern Yellow Sea
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Dade Song1, 2, Jintao Wang1, Xinjun Chen1, Xiaming Zhong2, Ying Xiong2, *, Jianhua Tang2, Lei Wu2
Affiliations
  • 1 College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
  • 2 Jiangsu Marine Fisheries Research Institute, Nantong 226007, China
Published: 2020-12-25 doi: 10.3969/j.issn.0253-4193.2020.12.003
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In this paper, the time series analysis model ARIMA (1, 2, 0) was applied to simulate and predict the stock of small yellow croaker (Larimichthys polyactis) based on the monitoring catches data of canvas stow net in the southern Yellow Sea from 2003 to 2014 and verified by the monitoring catches data of 2015 and 2016. The results showed that the simulated and actual values for the catch yield from 2003 to 2014 were correlated significantly (p<0.05) and the correlation coefficient was 0.881. The relative error between predicted and actual value in 2015 and 2016 were respectively 6.73% and 22.75%, the overall relative error was 14.74% and the regression equation fitted the real situation better, which illustrated that the time series analysis model ARIMA (1, 2, 0) can be applied to simulate the catches trend of L. polyactis and predict the catch stock, especially superior in short-term forecasting. However, in any case the fixed model of L. polyactis is not always suitable for all data analysis, and the values of p, d and q in ARIMA model are considered to be variable according to different time series. Therefore, the optimal values of p, d and q should be determined based on the guidance and analysis of relevant theories in order to avoid copying directly the fixed model.

small yellow croaker  /  stock  /  time series analysis  /  ARIMA model  /  the southern Yellow Sea
Dade Song, Jintao Wang, Xinjun Chen, Xiaming Zhong, Ying Xiong, Jianhua Tang, Lei Wu. 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
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Year 2020 volume 42 Issue 12
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Article Info
doi: 10.3969/j.issn.0253-4193.2020.12.003
  • Receive Date：2020-03-25
  • Online Date：2026-03-27
  • Published：2020-12-25
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  • Received：2020-03-25
  • Revised：2020-07-01
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Affiliations
    1 College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
    2 Jiangsu Marine Fisheries Research Institute, Nantong 226007, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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