
A Novel Simultaneous Grey Model SGM(1, 2) and Its Applications in Prediction
Maolin CHENG, Bin LIU
Journal of Systems Science and Information ›› 2022, Vol. 10 ›› Issue (5) : 466-483.
A Novel Simultaneous Grey Model SGM(1, 2) and Its Applications in Prediction
The common models used for grey system predictions include the GM(1, 1), the GM(1, N), the GM(N, 1), and so on, but their whitening equations are all single ordinary differential equations. However, objects and factors generally form a whole through mutual restrictions and connections when the objective world is developing and changing continuously. In other words, variables affect each other. The relationship can't be properly reflected by the single differential equation. Therefore, the paper proposes a novel simultaneous grey model. The paper gives a modeling method of simultaneous grey model SGM(1, 2) with 2 interactive variables. The example proves that the simultaneous grey model has high precision and improves the precision significantly compared with the conventional single grey model. The new method proposed enriches the grey modeling method system and has important significance for the in-depth study, popularization and application of grey models.
simultaneous grey model / whitening equation / time response equation / prediction precision {{custom_keyword}} /
Table 1 Related data of China's GDP & energy consumption and modeling results of GM(1, 1) |
Year | No. | x1(0) | x2(0) | GM (1, 1) Model | |||
Simulation Value of x1(0) | Relative Error (%) | Simulation Value of x2(0) | Relative Error (%) | ||||
2002 | 1 | 121717.4 | 169577.0 | - | - | - | - |
2003 | 2 | 137422.0 | 197083.0 | 181946.65 | 32.4 | 244298.64 | 24.0 |
2004 | 3 | 161840.2 | 230281.0 | 203846.07 | 26.0 | 256604.56 | 11.4 |
2005 | 4 | 187318.9 | 261369.0 | 228381.34 | 21.9 | 269530.35 | 3.12 |
2006 | 5 | 219438.5 | 286467.0 | 255869.71 | 16.6 | 283107.25 | 1.17 |
2007 | 6 | 270092.3 | 311442.0 | 286666.64 | 6.14 | 297368.05 | 4.52 |
2008 | 7 | 319244.6 | 320611.0 | 321170.33 | 0.603 | 312347.21 | 2.58 |
2009 | 8 | 348517.7 | 336126.0 | 359826.95 | 3.24 | 328080.89 | 2.39 |
2010 | 9 | 412119.3 | 360648.0 | 403136.35 | 2.18 | 344607.13 | 4.45 |
2011 | 10 | 487940.2 | 387043.0 | 451658.54 | 7.44 | 361965.83 | 6.48 |
2012 | 11 | 538580.0 | 402138.0 | 506020.94 | 6.05 | 380198.93 | 5.46 |
2013 | 12 | 592963.2 | 416913.0 | 566926.5 | 4.39 | 399350.47 | 4.21 |
2014 | 13 | 643563.1 | 428333.99 | 635162.76 | 1.31 | 419466.72 | 2.07 |
2015 | 14 | 688858.2 | 434112.78 | 711612.06 | 3.3 | 440596.28 | 1.49 |
2016 | 15 | 746395.1 | 441491.81 | 797262.92 | 6.82 | 462790.19 | 4.82 |
2017 | 16 | 832035.9 | 455826.92 | 893222.87 | 7.35 | 486102.06 | 6.64 |
Prediction Value | Relative Error (%) | Prediction Value | Relative Error (%) | ||||
2018 | 17 | 919281.1 | 471925.15 | 1000732.73 | 8.86 | 510588.2 | 8.19 |
2019 | 18 | 990865.1 | 487000.0 | 1121182.66 | 13.2 | 536307.77 | 10.1 |
2020 | 19 | 1015986.0 | 493000.0 | 1256130.15 | 23.6 | 563322.9 | 14.3 |
Average Simulation Relative Error(2002–2017) | - | 9.71 | - | 5.65 | |||
Average Prediction Relative Error (2018–2020) | - | 15.22 | - | 10.86 | |||
Average Relative Error (2002–2020) | - | 10.63 | - | 6.52 |
Table 2 Modeling results of simultaneous grey model of China's GDP and energy consumption |
Year | No. | x1(0) | x2(0) | Simultaneous Grey Model | |||
Simulation Value of x1(0) | Relative Error (%) | Simulation Value of x2(0) | Relative Error (%) | ||||
2002 | 1 | 121717.4 | 169577.0 | - | - | - | - |
2003 | 2 | 137422.0 | 197083.0 | 139373.05 | 1.42 | 198812.87 | 0.878 |
2004 | 3 | 161840.2 | 230281.0 | 157739.99 | 2.53 | 229640.58 | 0.278 |
2005 | 4 | 187318.9 | 261369.0 | 186944.67 | 0.2 | 257214.68 | 1.59 |
2006 | 5 | 219438.5 | 286467.0 | 224180.88 | 2.16 | 282215.39 | 1.48 |
2007 | 6 | 270092.3 | 311442.0 | 267358.58 | 1.01 | 305127.62 | 2.03 |
2008 | 7 | 319244.6 | 320611.0 | 314919.73 | 1.35 | 326288.21 | 1.77 |
2009 | 8 | 348517.7 | 336126.0 | 365701.26 | 4.93 | 345920.69 | 2.91 |
2010 | 9 | 412119.3 | 360648.0 | 418833.18 | 1.63 | 364160.74 | 0.974 |
2011 | 10 | 487940.2 | 387043.0 | 473662.68 | 2.93 | 381074.57 | 1.54 |
2012 | 11 | 538580.0 | 402138.0 | 529697.6 | 1.65 | 396672.02 | 1.36 |
2013 | 12 | 592963.2 | 416913.0 | 586564.36 | 1.08 | 410915.75 | 1.44 |
2014 | 13 | 643563.1 | 428333.99 | 643976.47 | 0.0642 | 423727.26 | 1.08 |
2015 | 14 | 688858.2 | 434112.78 | 701711.03 | 1.87 | 434990.57 | 0.202 |
2016 | 15 | 746395.1 | 441491.81 | 759591.07 | 1.77 | 444554.08 | 0.694 |
2017 | 16 | 832035.9 | 455826.92 | 817472.26 | 1.75 | 452230.82 | 0.789 |
Prediction Value | Relative Error (%) | Prediction Value | Relative Error (%) | ||||
2018 | 17 | 919281.1 | 471925.15 | 875232.82 | 4.79 | 457797.53 | 2.99 |
2019 | 18 | 990865.1 | 487000.0 | 932765.74 | 5.86 | 460992.48 | 5.34 |
2020 | 19 | 1015986.0 | 493000.0 | 989972.83 | 2.56 | 461512.5 | 6.39 |
Average Simulation Relative Error (2002–2017) | - | 1.75 | - | 1.26 | |||
Average Prediction Relative Error (2018–2020) | - | 4.40 | - | 4.90 | |||
Average Relative Error (2002–2020) | - | 2.19 | - | 1.87 |
Table 3 Modeling results of Grey models proposed by other references |
Year | No. | Grey Bernoulli Model Proposed by Ma and Wang[ | Grey Difference Equation Model Proposed by Cheng and Shi[ | ||||||
Simulation Value of x1(0) | Relative Error (%) | Simulation Value of x2(0) | Relative Error (%) | Simulation Value of x1(0) | Relative Error (%) | Simulation Value of x2(0) | Relative Error (%) | ||
2002 | 1 | - | - | - | - | - | - | - | - |
2003 | 2 | 171051.9 | 24.5 | 213968.7 | 8.57 | 137422.0 | 0 | 183299.2 | 6.99 |
2004 | 3 | 198757.58 | 22.8 | 243352.57 | 5.68 | 171685.82 | 6.08 | 218522.96 | 5.11 |
2005 | 4 | 226961.01 | 21.2 | 266785.94 | 2.07 | 207737.06 | 10.9 | 249454.19 | 4.56 |
2006 | 5 | 257110.69 | 17.2 | 287285.85 | 0.286 | 245706.67 | 12.0 | 276764.3 | 3.39 |
2007 | 6 | 289932.32 | 7.35 | 306078.79 | 1.72 | 285734.16 | 5.79 | 301022.85 | 3.35 |
2008 | 7 | 325971.25 | 2.11 | 323796.04 | 0.993 | 327968.2 | 2.73 | 322713.0 | 0.656 |
2009 | 8 | 365726.81 | 4.94 | 340809.18 | 1.39 | 372567.28 | 6.9 | 342244.71 | 1.82 |
2010 | 9 | 409701.88 | 0.587 | 357357.32 | 0.912 | 419700.3 | 1.84 | 359965.83 | 0.189 |
2011 | 10 | 458426.9 | 6.05 | 373604.61 | 3.47 | 469547.35 | 3.77 | 376171.63 | 2.81 |
2012 | 11 | 512474.62 | 4.85 | 389669.48 | 3.1 | 522300.44 | 3.02 | 391112.76 | 2.74 |
2013 | 12 | 572471.07 | 3.46 | 405640.9 | 2.7 | 578164.26 | 2.5 | 405002.04 | 2.86 |
2014 | 13 | 639105.21 | 0.693 | 421587.99 | 1.57 | 637357.13 | 0.964 | 418020.26 | 2.41 |
2015 | 14 | 713138.23 | 3.52 | 437565.98 | 0.795 | 700111.83 | 1.63 | 430321.06 | 0.873 |
2016 | 15 | 795413.03 | 6.57 | 453620.16 | 2.75 | 766676.62 | 2.72 | 442035.01 | 0.123 |
2017 | 16 | 886864.4 | 6.59 | 469788.47 | 3.06 | 837316.29 | 0.635 | 453273.2 | 0.56 |
Prediction Value | Relative Error (%) | Prediction Value | Relative Error (%) | Prediction Value | Relative Error (%) | Prediction Value | Relative Error (%) | ||
2018 | 17 | 988529.91 | 7.53 | 486103.38 | 3.00 | 912313.27 | 0.758 | 464130.18 | 1.65 |
2019 | 18 | 1101562.0 | 11.2 | 502593.13 | 3.20 | 991968.8 | 0.111 | 474686.46 | 2.53 |
2020 | 19 | 1227241.0 | 20.8 | 519282.75 | 5.33 | 1076604.2 | 5.97 | 485010.71 | 1.62 |
Average Simulation Relative Error (2002–2017) | - | 8.82 | - | 2.61 | - | 4.09 | - | 2.56 | |
Average Prediction Relative Error (2018–2020) | - | 13.17 | - | 3.85 | - | 2.28 | - | 1.93 | |
Average Relative Error (2002–2020) | - | 9.55 | - | 2.82 | - | 3.79 | - | 2.46 |
Table 4 Modeling results of the grey model GM(1, 1) of China's private car ownership and the added value of the transportation industry |
Year | No. | x1(0) | x2(0) | GM (1, 1) Model | |||
Simulation Value of x1(0) | Relative Error (%) | Simulation Value of x2(0) | Relative Error (%) | ||||
2005 | 1 | 1848.07 | 10668.8 | - | - | - | - |
2006 | 2 | 2333.32 | 12186.3 | 2931.301 | 25.6 | 13152.26 | 7.93 |
2007 | 3 | 2876.22 | 14605.1 | 3504.56 | 21.8 | 14455.08 | 1.03 |
2008 | 4 | 3501.39 | 16367.6 | 4189.929 | 19.7 | 15886.95 | 2.94 |
2009 | 5 | 4574.91 | 16522.4 | 5009.331 | 9.5 | 17460.66 | 5.68 |
2010 | 6 | 5938.71 | 18783.6 | 5988.98 | 0.846 | 19190.26 | 2.16 |
2011 | 7 | 7326.79 | 21842.0 | 7160.214 | 2.27 | 21091.18 | 3.44 |
2012 | 8 | 8838.6 | 23763.2 | 8560.499 | 3.15 | 23180.4 | 2.45 |
2013 | 9 | 10501.68 | 26042.7 | 10234.63 | 2.54 | 25476.57 | 2.17 |
2014 | 10 | 12339.36 | 28534.4 | 12236.17 | 0.836 | 28000.2 | 1.87 |
2015 | 11 | 14099.1 | 30519.5 | 14629.13 | 3.76 | 30773.8 | 0.833 |
2016 | 12 | 16330.2 | 33028.7 | 17490.07 | 7.1 | 33822.15 | 2.4 |
Prediction Value | Relative Error (%) | Prediction Value | Relative Error (%) | ||||
2017 | 13 | 18515.1 | 37121.9 | 20910.51 | 12.9 | 37172.46 | 0.136 |
2018 | 14 | 20574.93 | 40337.2 | 24999.87 | 21.5 | 40854.64 | 1.28 |
2019 | 15 | 22508.99 | 42466.3 | 29888.97 | 32.8 | 44901.56 | 5.73 |
Average Relative Error of the Simulation (2005–2016) | - | 8.83 | - | 2.99 | |||
Average Relative Error of the Prediction (2017–2019) | - | 22.41 | - | 2.38 | |||
Average Relative Error (2005–2019) | - | 11.74 | - | 2.86 |
Table 5 Modeling results of simultaneous grey model of China's private car ownership and the added value of transportation industry |
Year | No. | x1(0) | x2(0) | Simultaneous Grey Model | |||
Simulation Value of x1(0) | Relative Error (%) | Simulation Value of x2(0) | Relative Error (%) | ||||
2005 | 1 | 1848.07 | 10668.8 | - | - | - | - |
2006 | 2 | 2333.32 | 12186.3 | 2306.964 | 1.13 | 12590.42 | 3.32 |
2007 | 3 | 2876.22 | 14605.1 | 2869.723 | 0.226 | 14033.48 | 3.91 |
2008 | 4 | 3501.39 | 16367.6 | 3671.725 | 4.86 | 15667.54 | 4.28 |
2009 | 5 | 4574.91 | 16522.4 | 4687.336 | 2.46 | 17467.22 | 5.72 |
2010 | 6 | 5938.71 | 18783.6 | 5894.781 | 0.74 | 19410.4 | 3.34 |
2011 | 7 | 7326.79 | 21842.0 | 7275.696 | 0.697 | 21477.78 | 1.67 |
2012 | 8 | 8838.6 | 23763.2 | 8814.736 | 0.27 | 23652.52 | 0.466 |
2013 | 9 | 10501.68 | 26042.7 | 10499.24 | 0.0232 | 25919.92 | 0.471 |
2014 | 10 | 12339.36 | 28534.4 | 12318.96 | 0.165 | 28267.1 | 0.937 |
2015 | 11 | 14099.1 | 30519.5 | 14265.79 | 1.18 | 30682.8 | 0.535 |
2016 | 12 | 16330.2 | 33028.7 | 16333.59 | 0.0208 | 33157.13 | 0.389 |
Prediction Value | Relative Error (%) | Prediction Value | Relative Error (%) | ||||
2017 | 13 | 18515.1 | 37121.9 | 18518.02 | 0.0158 | 35681.41 | 3.88 |
2018 | 14 | 20574.93 | 40337.2 | 20816.38 | 1.17 | 38247.98 | 5.18 |
2019 | 15 | 22508.99 | 42466.3 | 23227.5 | 3.19 | 40850.06 | 3.81 |
Average Simulation Relative Error (2005–2016) | - | 1.07 | - | 2.27 | |||
Average Prediction Relative Error (2017–2019) | - | 1.46 | - | 4.28 | |||
Average Relative Error (2005–2019) | - | 1.15 | - | 2.70 |
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