کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
495670 | 862833 | 2013 | 9 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Construction of a new BRB based model for time series forecasting Construction of a new BRB based model for time series forecasting](/preview/png/495670.png)
• A new BRB based forecasting model that can use the hybrid information is proposed.
• A model selection criterion is defined to determine the delay step of the BRB based forecasting model.
• An optimal algorithm for training the parameters of the forecasting model is further developed.
• A case study is examined to demonstrate the ability of the newly proposed forecasting model.
It is important to predict the future behavior of complex systems. Currently there are no effective methods to solve time series forecasting problem by using the quantitative and qualitative information. Therefore, based on belief rule base (BRB), this paper focuses on developing a new model that can deal with the problem. Although it is difficult to obtain accurately and completely quantitative information, some qualitative information can be collected and represented by a BRB. As such, a new BRB based forecasting model is proposed when the quantitative and qualitative information exist simultaneously. The performance of the proposed model depends on the structure and belief degrees of BRB simultaneously. Moreover, the structure is determined by the delay step. In order to obtain the appropriate delay step using the available information, a model selection criterion is defined according to Akaike's information criterion (AIC). Based on the proposed model selection criterion and the optimal algorithm for training the belief degrees, an algorithm for constructing the BRB based forecasting model is developed. Experimental results show that the constructed BRB based forecasting model can not only predict the time series accurately, but also has the appropriate structure.
In order to obtain the appropriate delay step using the available information, a model selection criterion is defined according to Akaike's information criterion (AIC). Based on the proposed model selection criterion and the optimal algorithms for training the belief degrees, an algorithm for constructing the BRB based forecasting model is developed.Figure optionsDownload as PowerPoint slide
Journal: Applied Soft Computing - Volume 13, Issue 12, December 2013, Pages 4548–4556