{"title":"基于优化滚动灰色模型的时间序列预测","authors":"M. Yeh, Hung-Ching Lu, Ti-Hung Chen","doi":"10.1109/ICMLC48188.2019.8949310","DOIUrl":null,"url":null,"abstract":"This study attempts to improve the forecasting accuracy of rolling grey model by applying Gaussian bare-bones differential evolution (GBDE) to optimize the weight of background value and number of data points used to construct a rolling-GM(1,1). Experimental results on two real time series forecasting problems show that the proposed GBDE-based rolling-GM(l,l) outperforms the traditional rolling-GM(l,l) in terms of fitting accuracy and forecasting accuracy.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"19 4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Time Series Forecasting Using Optimized Rolling Grey Model\",\"authors\":\"M. Yeh, Hung-Ching Lu, Ti-Hung Chen\",\"doi\":\"10.1109/ICMLC48188.2019.8949310\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study attempts to improve the forecasting accuracy of rolling grey model by applying Gaussian bare-bones differential evolution (GBDE) to optimize the weight of background value and number of data points used to construct a rolling-GM(1,1). Experimental results on two real time series forecasting problems show that the proposed GBDE-based rolling-GM(l,l) outperforms the traditional rolling-GM(l,l) in terms of fitting accuracy and forecasting accuracy.\",\"PeriodicalId\":221349,\"journal\":{\"name\":\"2019 International Conference on Machine Learning and Cybernetics (ICMLC)\",\"volume\":\"19 4 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 International Conference on Machine Learning and Cybernetics (ICMLC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMLC48188.2019.8949310\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLC48188.2019.8949310","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Time Series Forecasting Using Optimized Rolling Grey Model
This study attempts to improve the forecasting accuracy of rolling grey model by applying Gaussian bare-bones differential evolution (GBDE) to optimize the weight of background value and number of data points used to construct a rolling-GM(1,1). Experimental results on two real time series forecasting problems show that the proposed GBDE-based rolling-GM(l,l) outperforms the traditional rolling-GM(l,l) in terms of fitting accuracy and forecasting accuracy.
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