本文關(guān)鍵詞： 網(wǎng)絡(luò)搜索 旅游預(yù)測(cè) CLSI指數(shù)合成 EMD經(jīng)驗(yàn)?zāi)B(tài)分解 BP神經(jīng)網(wǎng)絡(luò)　出處：《系統(tǒng)工程理論與實(shí)踐》2017年01期 　論文類(lèi)型：期刊論文

【摘要】：準(zhǔn)確的旅游預(yù)測(cè)對(duì)于旅游政策制定當(dāng)局和游客都具有重要意義,可以幫助資源的合理配置并避免擁堵事件和游客滯留事件的發(fā)生.為了提高旅游預(yù)測(cè)的準(zhǔn)確性,本文考慮噪聲在預(yù)測(cè)中的干擾,提出一種基于網(wǎng)絡(luò)搜索的CLSI-EMD-BP預(yù)測(cè)模型.該模型首先利用CLSI方法對(duì)網(wǎng)絡(luò)搜索數(shù)據(jù)進(jìn)行指數(shù)合成,并利用EMD對(duì)序列進(jìn)行噪聲處理,將高頻噪聲從原序列中分離,再利用去噪處理后的網(wǎng)絡(luò)搜索數(shù)據(jù)對(duì)旅游客流量進(jìn)行預(yù)測(cè).實(shí)證分析以九寨溝為例對(duì)預(yù)測(cè)期內(nèi)未來(lái)22周旅游客流量進(jìn)行預(yù)測(cè)發(fā)現(xiàn),基于網(wǎng)絡(luò)搜索的CLSI-EMD-BP預(yù)測(cè)誤差顯著低于時(shí)間序列、網(wǎng)絡(luò)搜索和BP神經(jīng)網(wǎng)絡(luò)三個(gè)基準(zhǔn)模型.該結(jié)論一方面說(shuō)明了本文預(yù)測(cè)模型的改進(jìn)作用,另一方面也表明了噪聲處理在預(yù)測(cè)中的必要性.
[Abstract]:Accurate tourism prediction is of great significance for both tourism policy making authorities and tourists. It can help the rational allocation of resources and avoid the occurrence of congestion events and tourist stranded events, in order to improve the accuracy of tourism prediction. In this paper, considering the interference of noise in prediction, a CLSI-EMD-BP prediction model based on network search is proposed. Firstly, the CLSI method is used to synthesize the network search data exponentially, and the EMD is used to deal with the noise of the sequence. The high frequency noise is separated from the original sequence, and then the network search data after denoising is used to predict the tourist flow. The empirical analysis takes Jiuzhaigou as an example to predict the tourist flow in the next 22 weeks in the forecast period. The prediction error of CLSI-EMD-BP based on network search is significantly lower than that of time series, and the network search and BP neural network are three benchmark models. On the other hand, it also shows the necessity of noise processing in prediction.
【作者單位】： 中國(guó)科學(xué)院大學(xué)經(jīng)濟(jì)與管理學(xué)院;中國(guó)科學(xué)院大數(shù)據(jù)挖掘與知識(shí)管理重點(diǎn)實(shí)驗(yàn)室;
【基金】：國(guó)家自然科學(xué)基金(71202115,71202155,71172199)~~
【分類(lèi)號(hào)】：F592.7

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本文編號(hào)：1523839