本文選題：景氣指標(biāo) + 峰谷對(duì)應(yīng)�。� 參考：《統(tǒng)計(jì)與決策》2017年06期

【摘要】：景氣指標(biāo)分類是景氣預(yù)測(cè)法中至關(guān)重要的環(huán)節(jié)、在以往的文獻(xiàn)中,一般采用一種方法對(duì)備選指標(biāo)進(jìn)行景氣分類,但其結(jié)果還存在優(yōu)化的空間。文章采用四種典型的景氣指標(biāo)分類方法:峰谷對(duì)應(yīng)法、時(shí)差相關(guān)分析法、K-L信息量法、灰色關(guān)聯(lián)度指標(biāo)分類法對(duì)同樣的多個(gè)指標(biāo)進(jìn)行分類。按照初定方案對(duì)四種方法的結(jié)果進(jìn)行綜合分析,對(duì)結(jié)果進(jìn)行優(yōu)化調(diào)整,應(yīng)用實(shí)例表明:四種方法的優(yōu)化調(diào)整結(jié)果比單一方法的結(jié)果更合理、更有效、更具有科學(xué)性。
[Abstract]:The classification of boom index is an important link in the method of climate prediction. In the previous literature, a method is generally used to classify the climate of alternative indicators, but there is still room for optimization of the results. This paper adopts four typical classification methods of boom index: peak and valley correspondence method, time difference correlation analysis method and K-L information method, grey correlation index classification method to classify the same multiple indexes. The results of the four methods are analyzed synthetically according to the initial scheme, and the results are optimized and adjusted. The application examples show that the results of the four methods are more reasonable, effective and scientific than the results of the single method.
【作者單位】： 華南理工大學(xué)數(shù)學(xué)學(xué)院;廣東工貿(mào)職業(yè)技術(shù)學(xué)院機(jī)械工程系;
【基金】：教育部人文社會(huì)科學(xué)規(guī)劃基金項(xiàng)目(13YJCZH030) 2015年廣東省高等學(xué)校質(zhì)量工程教改項(xiàng)目 華南理工大學(xué)中央高�；究蒲袠I(yè)務(wù)費(fèi)面上項(xiàng)目(2014XMS04);華南理工大學(xué)中央高�；究蒲袠I(yè)務(wù)費(fèi)重點(diǎn)項(xiàng)目(x2lxD2152360);華南理工大學(xué)校級(jí)教改項(xiàng)目(x2lxY1151210)
【分類號(hào)】：O213

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