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Gold Science and Technology >

2020 , Vol. 28 >Issue 1: 158 - 166

DOI: https://doi.org/10.11872/j.issn.1005-2518.2020.01.095

Mining Technology and Mine Management

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

  • Mei WANG ,
  • Jianhong CHEN ,
  • Shan YANG
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  • School of Resources and Safety Engineering，Central South University，Changsha 410083，Hunan，China

Received date: 2019-06-26

  Revised date: 2019-08-08

  Online published: 2020-02-26

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Highlights

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）C prediction 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）C model 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）C model was predicted directly with all 16 known data.By comparing the grey SCGM（1，1）C prediction model，the equal dimensional dynamic SCGM（1，1）C model and the equal dimensional dynamic Markov SCGM（1，1）C prediction model it is know that the accuracy of the equal dimensional dynamic SCGM（1，1）C model is higher than the SCGM（1，1）C model.The fitting accuracy of the equal dimensional dynamic Markov SCGM（1，1）C is 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）C model 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）C model 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）C model 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.

Cite this article

Mei WANG , Jianhong CHEN , Shan YANG . Gold Price Forecast Based on the Equal Dimensional Dynamic Markov SCGM（1，1）C Model[J]. Gold Science and Technology, 2020 , 28(1) : 158 -166 . DOI: 10.11872/j.issn.1005-2518.2020.01.095

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References

Publishing order | Descend order by publishing year | Descend order by cited within
1
刘亚非，陈燕武.试析黄金市场的灰色—马尔可夫预测[J].企业导报，2011（20）：224-226.

Liu Yafei， Chen Yanwu.An analysis of grey Markov forecasting in gold market[J].Guide to Business，2011（20）：224-226.

2
林石洁,张卫萍.黄金价格影响因素分析[J].中国有色金属，2018(17)：64-65.

Lin Shijie， Zhang Weiping.Analysis of the factors influencing gold price[J].China Nonferrous Metals，2018(17)：64-65.

3
张延利，张德生.基于动态数据驱动的改进灰色马尔科夫模型黄金价格预测[J].数学的实践与认识，2016，46（13）：31-38.

Zhang Yanli， Zhang Desheng.Improves grey Markov model forecasting the price of gold based on dynamic data driven[J].Mathematics in Practice and Theory，2016，46（13）：31-38.

4
陈晓珊，田良辉，韩晓茹.黄金价格预测分析与研究[J].佛山科学技术学院学报（自然科学版），2018，36（4）：6-10.

Chen Xiaoshan， Tian Lianghui， Han Xiaoru.Forecasting and analyzing gold price[J].Journal of Foshan University（Natural Science Edition），2018，36（4）：6-10.

5
吕海侠.基于相关系数变权重组合模型的黄金价格预测[J].黄金，2017，38（7）：3-5.

Haixia Lü.Gold price prediction based on correlation coefficient and variable weight combination model[J].Gold，2017，38（7）：3-5.

6
张均东，刘澄，孙彬.基于人工神经网络算法的黄金价格预测问题研究[J].经济问题，2010（1）：110-114.

Zhang Jundong， Liu Cheng， Sun Bin.The study on the application of ANFIS in stock index prediction[J].On Economic Problems，2010（1）：110-114.

7
张坤，郁湧，李彤.小波神经网络在黄金价格预测中的应用[J].计算机工程与应用，2010，46（27）：224-226，241.

Zhang Kun， Yu Yong， Li Tong.Application of wavelet neural network in prediction of gold price[J].Computer Engineering and Applications，2010，46（27）：224-226，241.

8
张品一，罗春燕，梁锶.基于GA-BP神经网络模型的黄金价格仿真预测[J].统计与决策，2018，34（17）：158-161.

Zhang Pinyi， Luo Chunyan， Liang Si.Gold price simulation prediction based on GA-BP neural network model[J].Statistics and Decision，2018，34（17）：158-161.

9
景志刚，施国良.基于小波分析的LS-SVM—ARIMA组合模型的黄金价格预测[J].黄金，2017，38（5）：5-8，14.

Jing Zhigang， Shi Guoliang.Gold price prediction using combined LS-SVM and ARIMA model based on wavelet analysis[J].Gold，2017，38（5）：5-8，14.

10
许贵阳.基于灰色预测方法的中国黄金期货价格预测模型[J].黄金，2014，35（1）：8-11.

Xu Guiyang.Forecasting model of China’s gold futures price based on gray prediction method[J].Gold，2014，35（1）：8-11.

11
刘成军，杨鹏，吕文生，等.灰色—马尔科夫复合模型在黄金价格预测中的应用[J].有色金属（矿山部分），2013，65（1）：7-11.

Liu Chengjun， Yang Peng， Wensheng Lü，et al.Application of Grey-Markov composite model in forecasting gold price[J].Non-ferrous Metals（Mining Section），2013，65（1）：7-11

12
张延利，杨丽.基于改进GM（1，1）模型的黄金价格预测[J].黄金，2015，36（7）：6-8.

Zhang Yanli， Yang Li.Gold price prediction based on improved GM（1，1） model[J].Gold，2015，36（7）：6-8.

13
Baur D G， Beckmann J， Czudaj R.A melting pot — Gold price forecasts under model and parameter uncertainty[J].International Review of Financial Analysis，2016，48：282-291.

14
Pierdzioch C， Rülke J， Stadtmann G.A note on forecasting the prices of gold and silver：Asymmetric loss and forecast rationality[J].The Quarterly Review of Economics and Finance，2013，53（3）：294-301.

15
兰建义，乔美英，周英.煤矿事故预测的马尔可夫SCGM（1，1）C模型的建立与应用[J].安全与环境学报，2016，16（5）：6-9.

Lan Jianyi， Qiao Meiying， Zhou Ying.Establishment and application of Markov S C G M （ 1 ， 1 ） C model for accidents forecast in coal mining practice[J].Journal of Safety and Environment，2016，16（5）：6-9.

16
刘思峰，谢乃明.灰色系统理论及其应用[M].北京：科学出版社，2008.

Liu Sifeng， Xie Naiming.Grey System Theory and Its Application[M].Beijing：Science Press，2008.

17
杨珊，明俊桦，周智勇.基于改进的非线性GM（1，1）模型的职业病预测研究[J].中国安全生产科学技术，2018，14（1）：111-116.

Yang Shan， Ming Junhua， Zhou Zhiyong.Study on prediction of occupational diseases based on improved nonlinear GM（1，1） model[J].Journal of Safety Science and Technology，2018，14（1）：111-116.

18
姜翔程，陈森发.加权马尔可夫SCGM（1，1）C模型在农作物干旱受灾面积预测中的应用[J].系统工程理论与实践，2009，29（9）：179-185.

Jiang Xiangcheng， Chen Senfa.Application of weighted Markov S C G M （ 1 ， 1 ） C model to predict drought crop area[J].Systems Engineering—Theory & Practice，2009，29（9）：179-185.

19
唐俊勇，田鹏辉，王辉.基于马尔可夫链与服务质量的网络可用性[J].计算机应用，2018，38（12）：3518-3523，3528.

Tang Junyong， Tian Penghui， Wang Hui.Network availability based on Markov chain and quality of service[J].Journal of Computer Applications，2018，38（12）：3518-3523，3528.

20
冉延平，何万生，雷旭晖，等.应用灰色GM（1.1）模型及其改进模型预测渭河天水段水质[J].水资源与水工程学报，2011，22（5）：88-91.

Ran Yanping， He Wansheng， Lei Xuhui，et al.Application of GM（1，1） model and improved model to predict the water quality of Weihe River in Tianshui section[J].Journal of Water Resources and Water Engineering，2011，22（5）：88-91.

21
徐建新，杨杰.煤矿百万吨死亡率动态无偏灰色马尔科夫预测[J].中国安全科学学报，2012，22（3）：122-127.

Xu Jianxin， Yang Jie.Application of dynamic unbiased grey Markov model in prediction of death rate per million-ton coal[J].China Safety Science Journal，2012，22（3）：122-127.

22
李大伟，徐浩军，刘东亮，等.改进的灰色马尔柯夫模型在飞行事故率预测中的应用［J］.中国安全科学学报，2009，19（9）：53-58.

Li Dawei， Xu Haojun， Liu Dongliang，et al.Improved grey Markov model and its application in prediction of flight accident rate[J].China Safety Science Journal，2009，19（9）：53-58.

23
杨灿生，黄国忠，陈艾吉，等.基于灰色—马尔科夫链理论的建筑施工事故预测研究［J］.中国安全科学学报，2011，21（10）：102-106．

Yang Cansheng， Huang Guozhong， Chen Aiji，et al.Research on construction accident forecast based on Gray-Markov theory[J].China Safety Science Journal，2011，21（10）：102-106.

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