通過(guò)網(wǎng)絡(luò)爬蟲(chóng)獲取網(wǎng)絡(luò)空間數(shù)據(jù)以分析和預(yù)測(cè)物理世界的宏觀事件是近年來(lái)十分重要的研究方向。本論文面向旅游業(yè)需求研究了如何利用谷歌趨勢(shì)統(tǒng)計(jì)的網(wǎng)絡(luò)搜索信息來(lái)預(yù)測(cè)物理世界的真實(shí)游客數(shù)量。世界各地的旅游業(yè)在迅猛發(fā)展中:荷蘭的旅游業(yè)占國(guó)民生產(chǎn)總值的9%,其首都阿姆斯特丹是一個(gè)非常美麗的城市,有著郁金香、溝渠、游艇和各種展覽館。有許多著名的畫家都生活在阿姆斯特丹,游客可以在這里的畫廊里發(fā)現(xiàn)許多備受贊譽(yù)的杰作。阿姆斯特丹的旅游業(yè)對(duì)整個(gè)荷蘭的經(jīng)濟(jì)發(fā)展貢獻(xiàn)很大。因此,準(zhǔn)確預(yù)測(cè)阿姆斯特丹的游客數(shù)量具有重要的實(shí)際意義。本文的研究思路是利用谷歌趨勢(shì)信息來(lái)預(yù)測(cè)阿姆斯特丹的旅游業(yè)需求。具體的,我們利用Touristjourney對(duì)搜索查詢?cè)~進(jìn)行拓展和篩選,然后通過(guò)GoogleSearch Query利用谷歌趨勢(shì)Google Trends返回的與查詢?cè)~相關(guān)的搜索統(tǒng)計(jì)信息分析真實(shí)游客數(shù)量的相關(guān)性并訓(xùn)練得到隱馬爾科夫模型;在測(cè)試階段,將搜索參數(shù)歸集到Google Trends中,獲得查詢?cè)~列表和對(duì)應(yīng)的Google Trends信息,進(jìn)而通過(guò)訓(xùn)練的隱馬爾科夫模型進(jìn)行游客數(shù)量預(yù)測(cè)。這項(xiàng)研究發(fā)現(xiàn),谷歌趨勢(shì)提供的信息對(duì)于確定阿姆斯...

【文章頁(yè)數(shù)】：61 頁(yè)

【學(xué)位級(jí)別】：碩士

【文章目錄】：
ACKNOWLEDGEMENT
摘要
ABSTRACT
ABBREVIATIONS AND ACRONYMS
1. INTRODUCTION
    1.1. TOURIST JOURNEY
    1.2. PROBLEM STATEMENT
    1.3. Research Objectives
    1.4. RESEARCH QUESTIONS
    1.5 THESIS OUTLINES
2. LITERATURE REVIEW
    2.1. RELATED WORK
        2.1.1. Econometric Models
        2.1.2. Artificial Intelligence
        2.1.3. Artificial Neural Networks (ANN)
    2.2 CONTRIBUTION AND DIFFERENCE WITH PREVIOUS WORKS
    2.3. BRIEF OVERVIEW OF SEARCH ENGINE AND SOCIAL MEDIA
        2.3.1. Social Media
        2.3.2. Search Engine
    2.4. FORECASTING BY THE CITY OF AMSTERDAM
3. METHODOLOGY
    3.1. THE WORKHOW OF OUR METHOD
    3.2. DATA COLLECTION
    3.3. HIDDEN MARKOV MODEL
    3.4. ARTIFICIAL NEURAL NETWORK (ANN)
    3.5. VECTOR AUTO-REGRESSIVE (VAR)
    3.6. HIDDEN MARKOV MODEL AS A SOLUTION
        3.6.1. Keywords Extraction and Evaluation
        3.6.2. Hidden Markov Model Training
        3.6.3. Hidden Markov Model Prediction
4. EXPERIMENTS AND RESULTS
    4.1. EXPERIMENTAL SETUP
    4.2. STATISTICS OF EXTRACTED KEYWORDS
    4.3. RESULTS OF GRANGER CAUSALITY ANALYSIS
    4.4. PREDICTION PERFORMANCE COMPARISON
5. CONCLUSION AND DISCUSSION
    5.1. CONCLUSIONS
    5.2. DISCUSSION
    5.3. LIMITATIONS AND FUTURE RESEARCH
REFERENCES
AUTHOR PROFILE
DATASET FOR THE MASTER'S THESIS



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