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  • 标题:Short-term Prediction of Stock Index Based on EMD and SVMs
  • 本地全文:下载
  • 作者:Wen Chen ; Yixiang Tian
  • 期刊名称:International Journal of Multimedia and Ubiquitous Engineering
  • 印刷版ISSN:1975-0080
  • 出版年度:2016
  • 卷号:11
  • 期号:8
  • 页码:1-12
  • 出版社:SERSC
  • 摘要:The green products in manufacture industry still differ from each other in terms of economics, technology, environmental friendliness, though they all reach the criteria of limitation. This paper expounds the necessity for product choice optimization and provides a model based on information entropy to judge different advantages of product groups. According to Entropy Weight Method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), the basic theory of green product evaluation model is established. The index system includes selected evaluation indicators of green products, considering about the evaluation index of the critical values to construct a hierarchycriterion, which uses entropy to objectively determine the weight vector of each evaluation index. Then, combined with TOPSIS analysis approach and calculates the pros and cons of green products. It is concluded that the evaluation results based on the Entropy-TOPSIS evaluation model are consistent with the actual results, and the results are consistent with the evaluation results of fuzzy mathematics. The method comprehensively considered many influential factors of the green products, to avoid the limitation of single criterion, and the importance of various factors are analyzed and compared, the prediction result is more scientific, as the theoretical basis of green products, and provide a reliable guidance method for product improvement. This is objective to empower the law, which also fully embodies the idea of variable weights and overcome the past green products that exist in the evaluation process homogeneity empowering. That means fixed weights lead to the limitation of a lack of flexible evaluation.
  • 关键词:Stock-index; ;EMD; IMF; SVMs; Combined Prediction
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