本文選題：收益管理　切入點(diǎn)：酒店　出處：《太原理工大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著我國經(jīng)濟(jì)實(shí)力的增強(qiáng),中國經(jīng)濟(jì)正在由工業(yè)主導(dǎo)向服務(wù)業(yè)主導(dǎo)加快轉(zhuǎn)變。酒店住宿業(yè)作為服務(wù)業(yè)的典型代表,其客容能力過剩和投資回收的壓力與日俱增,有效的管理對(duì)酒店業(yè)變的越來越重要。為了高效的管理酒店,提高酒店的收益,這里引入了收益管理的概念。它主要通過建立實(shí)時(shí)預(yù)測(cè)模型和對(duì)以市場(chǎng)細(xì)分為基礎(chǔ)的需求行為分析,確定最佳的銷售和服務(wù)價(jià)格。酒店的收益管理主要包括以下四個(gè)部分:需求預(yù)測(cè)、超量預(yù)訂、客房分配和定價(jià)系統(tǒng),其中,客戶需求預(yù)測(cè)是收益管理的基礎(chǔ)與核心。傳統(tǒng)的預(yù)測(cè)模型一般為歷史同期和時(shí)間序列,時(shí)間序列又分為移動(dòng)平均(Moving Average),指數(shù)平滑(Exponential Smoothing),卡爾曼濾波(Kalman Filters),自適應(yīng)濾波(Adaptive Filters)和自回歸積分滑動(dòng)平均模型(Autoregressive Integrated Moving Average Model),但酒店收益管理的預(yù)測(cè)和傳統(tǒng)的預(yù)測(cè)不同,一般有兩個(gè)時(shí)間變量,分別為預(yù)定時(shí)間和消費(fèi)時(shí)間。在大數(shù)據(jù)的背景下,如果能夠很好的針對(duì)酒店行業(yè)的數(shù)據(jù)特點(diǎn),對(duì)酒店需求預(yù)測(cè)進(jìn)行研究分析,就可以幫助酒店管理者更好的作出決策,從而提升酒店收益。本文提出了一套基于大數(shù)據(jù)的針對(duì)酒店微觀市場(chǎng)的預(yù)測(cè)方法,從數(shù)據(jù)預(yù)處理、數(shù)據(jù)篩選到最后的訓(xùn)練與預(yù)測(cè)。在數(shù)據(jù)的預(yù)處理階段,本文通過對(duì)數(shù)據(jù)的分析,將原始數(shù)據(jù)進(jìn)行了轉(zhuǎn)換,轉(zhuǎn)換后的數(shù)據(jù)經(jīng)過KS檢驗(yàn)符合高斯分布模型,因此采用了基于統(tǒng)計(jì)的異常點(diǎn)檢測(cè)方法,找出了間夜量特殊的日期。本文還設(shè)計(jì)了一種糾偏函數(shù),在保持了異常數(shù)據(jù)的相對(duì)關(guān)系同時(shí),可以令間夜量特殊的日期得到很好的處理。在數(shù)據(jù)篩選階段,本文對(duì)比了幾種經(jīng)典的分類方法,設(shè)計(jì)了一套對(duì)于每類單獨(dú)訓(xùn)練再將整體結(jié)果相結(jié)合的訓(xùn)練思路,這種思路在可以進(jìn)一步將模型精度提高的同時(shí),還有利于分析某類預(yù)測(cè)精度不佳的原因,具有很好的可解釋性。最后本文結(jié)合前人的研究成果,對(duì)經(jīng)典的方法進(jìn)行了改進(jìn),將經(jīng)典的基于時(shí)間序列的預(yù)測(cè)算法轉(zhuǎn)化成了更普適的機(jī)器學(xué)習(xí)方法,在與經(jīng)典預(yù)測(cè)算法的比較中,效果良好。隨著機(jī)器學(xué)習(xí)算法的不斷改進(jìn),機(jī)器學(xué)習(xí)理論的不斷完善,結(jié)合本文提供的思路,未來可以使用更多的機(jī)器學(xué)習(xí)模型對(duì)該問題進(jìn)行分析、訓(xùn)練和預(yù)測(cè)。
[Abstract]:With the strengthening of China's economic strength, China's economy is undergoing a rapid transformation from industry-led to service-oriented. As a typical representative of the service industry, hotel accommodation industry, as a typical representative of the service industry, is under increasing pressure of overcapacity and investment return. Effective management is becoming more and more important to the hotel industry. The concept of revenue management is introduced here. It is mainly through the establishment of real-time forecasting model and the analysis of demand behavior based on market segmentation. Determine the best price for sales and services. The revenue management of the hotel mainly consists of the following four parts: demand forecasting, overbooking, room allocation and pricing system, among which, Customer demand forecasting is the basis and core of revenue management. The time series are divided into moving average, exponential smoothing, Kalman filters, adaptive filters and autoregressive Integrated Moving Average models. However, the prediction of hotel revenue management is different from the traditional one. Generally, there are two time variables, one is the reservation time and the other is the consumption time. Under the background of big data, if we can do research and analysis on the hotel demand forecast according to the characteristics of the hotel industry data, This paper puts forward a set of forecasting methods based on big data for the hotel micro market, which can help the hotel managers to make better decisions and improve the hotel income. From the perspective of data preprocessing, this paper puts forward a set of forecasting methods for the hotel micro market. Data filter to the final training and prediction. In the data preprocessing stage, through the analysis of the data, the original data are converted, the converted data after KS test accord with Gao Si distribution model. Therefore, the outlier detection method based on statistics is used to find out the special date of the night volume. A correction function is designed, which keeps the relative relation of the abnormal data at the same time. In the stage of data screening, this paper compares several classical classification methods, and designs a set of training ideas for each kind of individual training and combining the overall results. This method can further improve the accuracy of the model, at the same time, it is helpful to analyze the causes of the poor prediction accuracy, and it has good interpretability. Finally, this paper improves the classical method combined with the previous research results. The classical prediction algorithm based on time series is transformed into a more general machine learning method. Compared with the classical prediction algorithm, the effect is good. With the continuous improvement of the machine learning algorithm, the machine learning theory is constantly improved. Combined with the ideas provided in this paper, more machine learning models can be used to analyze, train and predict the problem in the future.
【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F719.2;TP181

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