發(fā)布時(shí)間：2018-05-19 13:44

  本文選題：機(jī)票價(jià)格預(yù)測 + KNN算法�。� 參考：《中國民航大學(xué)》2013年碩士論文

【摘要】：隨著民航事業(yè)的快速發(fā)展,越來越多的旅客將航空運(yùn)輸作為遠(yuǎn)程出行的首選。而網(wǎng)絡(luò)技術(shù)的飛速發(fā)展以及電子客票全面推廣使用,各大航空公司都已利用各自的網(wǎng)站開始銷售電子客票,人們可以從Internet上快速便捷的獲取機(jī)票價(jià)格信息。面對(duì)頻繁變化的機(jī)票價(jià)格,人們渴望知道機(jī)票的變化規(guī)律及何時(shí)購買機(jī)票最劃算。本文基于國內(nèi)航線機(jī)票數(shù)據(jù)利用數(shù)據(jù)挖掘的算法建立模型,旨在給旅客提供按出行日期預(yù)測的機(jī)票價(jià)格及購買機(jī)票的建議。本文以國內(nèi)某一航班為研究對(duì)象,從數(shù)據(jù)挖掘的角度進(jìn)行探究。主要研究工作如下:一、機(jī)票數(shù)據(jù)采集,通過利用垂直搜索引擎HERTRIX工具獲取網(wǎng)站的機(jī)票價(jià)格,利用HTMLParser工具實(shí)現(xiàn)機(jī)票價(jià)格數(shù)據(jù)的在線獲取;二、簡述機(jī)票數(shù)據(jù)分析和預(yù)處理過程,將抓取到的數(shù)據(jù)進(jìn)行預(yù)處理,統(tǒng)一標(biāo)準(zhǔn)化格式,存入數(shù)據(jù)庫,并分析機(jī)票各個(gè)屬性與價(jià)格的關(guān)系;三、在詳細(xì)研究KNN、Q學(xué)習(xí)和加權(quán)移動(dòng)平均時(shí)間序列分析算法基本原理的基礎(chǔ)上,改進(jìn)了Q學(xué)習(xí)和時(shí)間序列算法,首先KNN算法用于訓(xùn)練購買決策分類器,給用戶一個(gè)購買建議;其次通過改進(jìn)Q學(xué)習(xí)算法建立機(jī)票價(jià)格預(yù)測模型,運(yùn)用歷史數(shù)據(jù)不斷訓(xùn)練Q矩陣,呈現(xiàn)給用戶預(yù)測價(jià)格;最后運(yùn)用改進(jìn)的加權(quán)移動(dòng)平均時(shí)間序列分析法建立機(jī)票預(yù)測模型,該模型分為小于一個(gè)星期和大于一個(gè)星期兩種情況,根據(jù)預(yù)測時(shí)間與當(dāng)前時(shí)間的時(shí)間差給用戶呈現(xiàn)預(yù)測價(jià)格;四、主觀Bayes算法的集成學(xué)習(xí)模型,利用Bayes推理技術(shù)將三種機(jī)票價(jià)格預(yù)測模型的預(yù)測結(jié)果進(jìn)行融合,得到集成的機(jī)票預(yù)測價(jià)格和最終的購買建議。將上述數(shù)據(jù)獲取技術(shù)、價(jià)格預(yù)測技術(shù)和集成算法結(jié)合,本文設(shè)計(jì)了機(jī)票價(jià)格預(yù)測原型系統(tǒng)。本文使用已抓取的深圳至北京的航班號(hào)為CA1304的9336條航班機(jī)票數(shù)據(jù),分別用KNN算法、Q學(xué)習(xí)算法、時(shí)間序列算法和主觀Bayes集成算法進(jìn)行預(yù)測。通過模擬實(shí)驗(yàn),主觀Bayes集成算法很好的實(shí)現(xiàn)了節(jié)省開支,其效果優(yōu)于其他三種算法。
[Abstract]:With the rapid development of civil aviation, more and more passengers take air transportation as the first choice for long-distance travel. With the rapid development of network technology and the comprehensive promotion and use of electronic ticketing, all major airlines have started to sell e-tickets using their own websites, and people can quickly and conveniently obtain ticket price information from Internet. In the face of frequent changes in ticket prices, people are eager to know the rules of change and when to buy the most cost-effective ticket. Based on domestic airline ticket data, this paper establishes a model by using data mining algorithm, which aims to provide passengers with the advice of ticket price forecast according to travel date and purchase of ticket. This article takes a domestic flight as the research object, carries on the research from the data mining angle. The main research work is as follows: first, air ticket data collection, through the use of vertical search engine HERTRIX tool to obtain the price of tickets, using HTMLParser tools to achieve online access to ticket price data; second, briefly air ticket data analysis and preprocessing process, The captured data is preprocessed, standardized format is unified, stored in database, and the relationship between each attribute of ticket and price is analyzed. Thirdly, on the basis of studying the basic principle of KNNQ learning and weighted moving average time series analysis algorithm in detail, Q learning and time series algorithm are improved. First, KNN algorithm is used to train the purchase decision classifier to give the user a purchase suggestion. Secondly, through the improved Q learning algorithm, the ticket price prediction model is established, and the Q matrix is trained with historical data. Finally, an improved weighted moving average time series analysis method is used to establish a ticket prediction model, which can be divided into two categories: less than one week and more than one week. According to the time difference between the forecast time and the current time, the forecast price is presented to the user. Fourthly, the integrated learning model of subjective Bayes algorithm is used to fuse the forecast results of the three ticket price prediction models by using the Bayes reasoning technology. Get integrated ticket forecast prices and final purchase advice. Combining the above data acquisition technology, price forecasting technology and integrated algorithm, a prototype system of ticket price prediction is designed in this paper. In this paper, the data of 9336 flights from Shenzhen to Beijing whose flight number is CA1304 are used to predict, respectively, using KNN algorithm Q learning algorithm, time series algorithm and subjective Bayes integration algorithm. Through simulation experiments, the subjective Bayes ensemble algorithm achieves good cost saving, and its effect is better than the other three algorithms.
【學(xué)位授予單位】：中國民航大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：F562.5;TP311.13

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