SCGM（1，1）C,预测精度,灰色模型," /> SCGM（1，1）C,预测精度,灰色模型,"/> Gold Price Forecast Based on the Equal Dimensional Dynamic Markov SCGM（1，1）<sub>C</sub> Model
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Gold Science and Technology ›› 2020, Vol. 28 ›› Issue (1): 158-166.doi: 10.11872/j.issn.1005-2518.2020.01.095

• Mining Technology and Mine Management • Previous Articles    

Gold Price Forecast Based on the Equal Dimensional Dynamic Markov SCGM（1，1）C Model

Mei WANG(),Jianhong CHEN(),Shan YANG   

  1. School of Resources and Safety Engineering，Central South University，Changsha 410083，Hunan，China
  • Received:2019-06-26 Revised:2019-08-08 Online:2020-02-29 Published:2020-02-26
  • Contact: Jianhong CHEN E-mail:1816057034@qq.com;cjh@263.net

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Abstract:

In order to improve the accuracy of gold price prediction，an equal dimensional dynamic Markov SCGM（1，1）C forecasting model was proposed.Prediction has high requirements for the selection of data，and the latest data can improve the prediction accuracy.The equal dimensional dynamic Markov SCGM（1，1）C model is a composite model which combines the equal dimensional dynamic SCGM（1，1）C model with the Markov chain.On the basis of the prediction results of the equal dimensional dynamic SCGM（1，1）C，the grey fitting accuracy index is divided into states，and the state of the monthly gold price is determined.On this basis，the next transition direction is determined according to the transition probability matrix，and finally the predicted data is obtained.In this paper，the data processing method of take the new one and remove the old one was introduced，and the equal dimension dynamic data optimization was used.Because the grey SCGM（1，1）Cprediction model is also a grey model，the grey model is characterized by less original data，so a large number of original values are not needed in this paper.A total of 16 groups of gold price data from January 2018 to April 2019 were selected，and the dimension of dynamic equal dimension was determined to be 13.When SCGM（1，1）Cmodel data were processed，13 gold price data from January 2018 to January 2019 were selected to predict the gold price in February 2019，and then the gold price of March 2019 and April 2019 was predicted as above.The prediction data from February 2019 to April 2019 were used as fitting data to observe whether the accuracy of the prediction model is the best.The grey SCGM（1，1）Cmodel was predicted directly with all 16 known data.By comparing the grey SCGM（1，1）Cprediction model，the equal dimensional dynamic SCGM（1，1）Cmodel and the equal dimensional dynamic Markov SCGM（1，1）Cprediction model it is know that the accuracy of the equal dimensional dynamic SCGM（1，1）Cmodel is higher than the SCGM（1，1）Cmodel.The fitting accuracy of the equal dimensional dynamic Markov SCGM（1，1）Cis the highest，reaching the first order，the average relative error is 0.85%，which meets the prediction requirements，and the gold price in May 2019 is predicted to be $1 314.78.Although the grey SCGM（1，1）Cmodel has the lowest accuracy，it is simple to calculate and all the predicted values can be obtained by one calculation.The equal dimensional dynamic Markov SCGM（1，1）Cmodel is the most complex，but its predict results are the most accurate.Compared with the neural network and other methods，the equal dimensional dynamic Markov SCGM（1，1）Cmodel is simpler，so the model can be used to predict the gold price.The gold price in May 2019 is $1 295.55.Which Contrast with the predict is very close.

Key words: gold price, equal dimensional dynamic, Markov, SCGM（1，1）C, forecast precision, gray model

CLC Number: 

  • TD679

Table 1

Process of equal dimensional dynamic prediction algorithm"

步骤输入值预测值
1x(0)(1),x(0)(2),?,x(0)(13)x∧(0)(14)
2x(0)(2),x(0)(3),?,x(0)(14)x∧(0)(15)
???
n+1x(0)(n+1),x(0)(n+2),?,x(0)(n+13)x∧(0)(n+14)

Table 2

Division of residual accuracy"

相对误差平均值范围/%精度等级相对误差平均值范围/%精度等级
0～1一级5～10三级
1～5二级10～20四级

Table 3

Gold prices in January 2018 to April 2019"

时间价格/（美元·盎司-1）时间价格/（美元·盎司-1）
2018-011 345.12018-091 187.2
2018-021 317.82018-101 215.0
2018-031 323.82018-111 217.6
2018-041 313.22018-121 279.0
2018-051 305.32019-011 323.2
2018-061 250.42019-021 319.2
2018-071 220.92019-031 295.4
2018-081 202.42019-041 282.3

Fig.1

Gold price in January 2018 to April 2019"

Table 4

Prediction results of gold price by Grey SCGM（1，1）C model"

时间黄金价格/（美元·盎司-1）原始值/预测值相对误差/%
原始值预测值
2018-011 345.11 281.31.0497-4.7
2018-021 317.81 277.91.0312-3.0
2018-031 323.81 274.51.0386-3.7
2018-041 313.21 271.11.0330-3.2
2018-051 305.31 267.71.0296-2.9
2018-061 250.41 264.40.98891.1
2018-071 220.91 261.00.96813.3
2018-081 202.41 257.60.95614.6
2018-091 187.21 254.30.94655.7
2018-101 215.01 251.00.97123.0
2018-111 217.61 247.60.97592.5
2018-121 279.01 244.31.0278-2.7
2019-011 323.21 241.01.0662-6.2
2019-021 319.21 237.31.0661-6.1
2019-031 295.41 234.41.0494-4.7
2019-041 282.31 231.11.0415-3.9
2019-05-1 250.3825--

Table 5

Prediction results of gold price by equal dimensional dynamic SCGM（1，1）C model"

时间黄金价格/（美元·盎司-1）相对误差/%
实际值预测值
平均相对误差/%-3.66
2019-021 319.21 219.05-7.5
2019-031 295.41 270.34-1.5
2019-041 282.31 250.55-2.5

Table 6

Statistical of gold price prediction results about equal dimensional dynamic SCGM（1，1）C model in January 2018 to January 2019"

时间黄金价格/（美元·盎司-1）原始值/预测值相对误差/%
原始值预测值
2018-011 345.11 266.61.0620-5.8
2018-021 317.81 263.41.0430-4.1
2018-031 323.81 260.31.0504-4.7
2018-041 313.21 257.11.0446-4.2
2018-051 305.31 253.91.0410-3.9
2018-061 250.41 250.81.00000.03
2018-071 220.91 247.70.97852.1
2018-081 202.41 244.50.96623.5
2018-091 187.21 241.40.95634.5
2018-101 215.01 238.30.98121.9
2018-111 217.61 235.20.98581.4
2018-121 279.01 232.11.0380-3.6
2019-011 323.21 229.01.0766-7.1

Table 7

State division according to grey fitting accuracy index"

编号灰拟合精度指标状态划分编号灰拟合精度指标状态划分
E10.9500～0.9700E41.0000～1.0420
E20.9700～0.9850E51.0420～1.1200
E30.9850～1.0000

Table 8

Prediction results of gold price by equal dimensional dynamic Markov SCGM（1，1）C model"

时间黄金价格/（美元·盎司-1）相对误差/%模型精度等级
实际值预测值
平均相对误差/%0.85一级
2019-021 319.21 317.73-0.11一级
2019-031 295.41 315.721.50二级
2019-041 282.31 294.300.94一级

Fig.2

Comparison between gold price predicted value and original value fitting"

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