20.500.12556/RUL-142977 Napovedovanje zasedenosti hotela z uporabo rezervacijskih oken Forecasting hotel occupancy using booking windows 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. 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. napovedovanje zasedenosti hotela strojno učenje regresijski modeli napovedovanje časovnih vrst forecasting hotel occupancy machine learning regression models time series forecasting true false false Slovenski jezik Angleški jezik Magistrsko delo/naloga 2022-12-07 11:10:00 2022-12-07 11:10:07 2023-01-06 08:27:03 0000-00-00 00:00:00 2022 0 0 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 33802 136475395 Savli_Maj_-_Napovedovanje_zasedenosti_hotela_z_uporabo_rezervacijskih_oken.pdf Savli_Maj_-_Napovedovanje_zasedenosti_hotela_z_uporabo_rezervacijskih_oken.pdf 1 45B84408DDDCFC4B2250349CF0A2A5B6 5a2a335c7c1107e3f8f44df2b09afb9b4357f4b5c41ceb9b24f2a708b9f71c28 2b63f4f7-7617-11ed-9c94-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=164569 Fakulteta za računalništvo in informatiko 0 0 0