Napovedovanje zasedenosti hotela z uporabo rezervacijskih oken

magistrsko delo

Abstract

Uspešnost večine sistemov za upravljanje prihodkov temelji na sistemu za napovedovanje povpraševanja. Bolje kot takšen sistem napoveduje povpraševanje, lažje je upravljati z omejenimi sredstvi in cenami, kar vodi do večjih prihodkov in boljšega izkoristka sredstev. V magistrskem delu predlagamo dva pristopa za napovedovanje povpraševanja, ki sta sestavljena iz enega oziroma več regresijskih modelov na podlagi rezervacijskih oken. Predlagani metodi primerjamo z vztrajnostnim modelom, statističnim modelom ARIMA in podobnim modelom iz sorodnega dela. V delu izvedemo eksperiment z domenskim ekspertom in njegove rezultate primerjamo z našima pristopoma. Metode testiramo na realnih podatkih konkretnega hotela, za katerega v delu razvijemo tudi aplikacijski programski vmesnik za pridobivanje napovedi o zasedenosti. Rezultati pokažejo, da sta predlagani metodi izmed uporabljenih najboljši, saj so napovedne napake manjše od napak ostalih metod ter napak domenskega eksperta.

Keywords

napovedovanje zasedenosti hotela;regresijski modeli;napovedovanje časovnih vrst;magisteriji;

Data

Language:	Slovenian
Year of publishing:	2022
Typology:	2.09 - Master's Thesis
Organization:	UL FRI - Faculty of Computer and Information Science
Publisher:	[M. Šavli]
UDC:	004.8:640.4(043.2)
COBISS:	136475395
Views:	58
Downloads:	21
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	English
Secondary title:	Forecasting hotel occupancy using booking windows
Secondary abstract:	The success of most revenue management systems is based solely on demand forecasting systems. The better such a system predicts demand, the easier it is to manage limited resources and prices, which leads to better utilization and higher revenue. In this master's thesis, we propose two different demand forecasting approaches, one of which consists of one model and the other of several regression models based on booking windows. We compare the proposed method with the persistence model, the statistical ARIMA model and a similar model from a related work. In the thesis we also conduct an experiment with a domain expert and compare his results with our approach. We test the methods on real data of a specific hotel, for which we also develop an application programming interface for obtaining demand forecasts. The results show that the proposed methods are the best among those considered, as the forecast errors are smaller than the errors of the other methods and the domain expert's errors.
Secondary keywords:	forecasting hotel occupancy;machine learning;regression models;time series forecasting;computer science;computer and information science;master's degree;Strojno učenje;Hotelirstvo;Računalništvo;Univerzitetna in visokošolska dela;
Type (COBISS):	Master's thesis/paper
Study programme:	1000471
Embargo end date (OpenAIRE):	1970-01-01
Thesis comment:	Univ. v Ljubljani, Fak. za računalništvo in informatiko
Pages:	102 str.
ID:	17341827