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dc.contributor.authorGao, YunLongzh_CN
dc.contributor.authorPan, JinYanzh_CN
dc.contributor.authorJi, GuoLizh_CN
dc.contributor.authorGao, Fengzh_CN
dc.contributor.author吉国力zh_CN
dc.date.accessioned2013-12-12T02:49:25Z
dc.date.available2013-12-12T02:49:25Z
dc.date.issued2011-05zh_CN
dc.identifier.citationScience China-Technological Sciences, 2011,54(5):1325-1337zh_CN
dc.identifier.issn1674-7321zh_CN
dc.identifier.otherWOS:000289737300032zh_CN
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/70690
dc.descriptionNational Natural Science Foundation of China [60974101]; Education Ministry of China [NCET-06-0828]zh_CN
dc.description.abstractThe forecasting of time-series data plays an important role in various domains. It is of significance in theory and application to improve prediction accuracy of the time-series data. With the progress in the study of time-series, time-series forecasting model becomes more complicated, and consequently great concern has been drawn to the techniques in designing the forecasting model. A modeling method which is easy to use by engineers and may generate good results is in urgent need. In this paper, a gradient-boost AR ensemble learning algorithm (AREL) is put forward. The effectiveness of AREL is assessed by theoretical analyses, and it is demonstrated that this method can build a strong predictive model by assembling a set of AR models. In order to avoid fitting exactly any single training example, an insensitive loss function is introduced in the AREL algorithm, and accordingly the influence of random noise is reduced. To further enhance the capability of AREL algorithm for non-stationary time-series, improve the robustness of algorithm, discourage overfitting, and reduce sensitivity of algorithm to parameter settings, a weighted kNN prediction method based on AREL algorithm is presented. The results of numerical testing on real data demonstrate that the proposed modeling method and prediction method are effective.zh_CN
dc.language.isoen_USzh_CN
dc.source.urihttp://dx.doi.org/10.1007/s11431-011-4340-1zh_CN
dc.subjectWIND-SPEEDzh_CN
dc.subjectAUTOREGRESSIVE MODELSzh_CN
dc.subjectAR MODELzh_CN
dc.subjectPOWERzh_CN
dc.subjectIDENTIFICATIONzh_CN
dc.subjectPREDICTIONSzh_CN
dc.titleA time-series modeling method based on the boosting gradient-descent theoryzh_CN
dc.typeArticlezh_CN


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