{"title":"中国CRCP指数预测模型研究","authors":"Cai Yun, Zheng Kai, L. Jing","doi":"10.1109/ICSSSM.2014.6943362","DOIUrl":null,"url":null,"abstract":"Coking coal plays a vital role in China's economic development as an important strategic resource. The article builds the coking coal price index prediction model by the method of time series model, using statistical software, Eviews6.0. The result shows that the CRCP price index sequence is stationary time series after the first order difference, and the model of ARIMA (2, 1, 3) is better to forecast.","PeriodicalId":206364,"journal":{"name":"2014 11th International Conference on Service Systems and Service Management (ICSSSM)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Study on forecast model of CRCP index in China\",\"authors\":\"Cai Yun, Zheng Kai, L. Jing\",\"doi\":\"10.1109/ICSSSM.2014.6943362\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Coking coal plays a vital role in China's economic development as an important strategic resource. The article builds the coking coal price index prediction model by the method of time series model, using statistical software, Eviews6.0. The result shows that the CRCP price index sequence is stationary time series after the first order difference, and the model of ARIMA (2, 1, 3) is better to forecast.\",\"PeriodicalId\":206364,\"journal\":{\"name\":\"2014 11th International Conference on Service Systems and Service Management (ICSSSM)\",\"volume\":\"100 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-06-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 11th International Conference on Service Systems and Service Management (ICSSSM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSSSM.2014.6943362\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 11th International Conference on Service Systems and Service Management (ICSSSM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSSSM.2014.6943362","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Coking coal plays a vital role in China's economic development as an important strategic resource. The article builds the coking coal price index prediction model by the method of time series model, using statistical software, Eviews6.0. The result shows that the CRCP price index sequence is stationary time series after the first order difference, and the model of ARIMA (2, 1, 3) is better to forecast.