Hotel price forecasting using time series. An exploratory research
[EN] This paper proposes the use of time-series-based forecasting methods to identify the main predictor variables of prices in hotels located in the city of Barcelona. However, in contrast to previous work, the research focusses on online prices, i.e. the prices set by hotel companies' rev...
| Autores: | , , |
|---|---|
| Tipo de recurso: | capítulo de libro |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/201701 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/201701 |
| Access Level: | acceso abierto |
| Palabra clave: | Hotel price Time series Autoregressive models Revenue management |
| id |
ES_b5484b322fffee9d2b632a0ec9d764fa |
|---|---|
| oai_identifier_str |
oai:riunet.upv.es:10251/201701 |
| network_acronym_str |
ES |
| network_name_str |
España |
| repository_id_str |
|
| spelling |
Hotel price forecasting using time series. An exploratory researchChávez-Miranda, EstherToral, SergioMartínez-Torres, M. RocíoHotel priceTime seriesAutoregressive modelsRevenue management[EN] This paper proposes the use of time-series-based forecasting methods to identify the main predictor variables of prices in hotels located in the city of Barcelona. However, in contrast to previous work, the research focusses on online prices, i.e. the prices set by hotel companies' revenue management algorithms, rather than purchase prices. For the training of the time series, a dataset of hotel prices offered on from Booking.com with a horizon of zero days in advance has been used. In addition to the price series itself, a set of exogenous variables has been included to improve the predictive capacity of the model. As a result, the relative importance of the lags of the endogenous variables and of the exogenous variables, as well as the prediction error, have been obtained. The lag is the main variable in the determination of the forecast and, more specifically, those referring to one day-, one week-, and one month-lags.This publication is part of the project TED2021-130406B-I00, funded by MCIN/AEI/10.13039/501100011033 and by the European Union "NextGenerationEU"/PRTREditorial Universitat Politècnica de ValènciaMinisterio de Ciencia, Innovación y UniversidadesEuropean CommissionRepositorio Institucional de la Universitat Politècnica de València Riunet20232023-09-22book parthttp://purl.org/coar/resource_type/c_3248VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/bookPartapplication/pdfhttps://riunet.upv.es/handle/10251/201701reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengMinisterio de Ciencia e Innovación http://dx.doi.org/10.13039/501100004837open accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Compartir igual (by-nc-sa) http://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2017012026-06-13T07:49:27Z |
| dc.title.none.fl_str_mv |
Hotel price forecasting using time series. An exploratory research |
| title |
Hotel price forecasting using time series. An exploratory research |
| spellingShingle |
Hotel price forecasting using time series. An exploratory research Chávez-Miranda, Esther Hotel price Time series Autoregressive models Revenue management |
| title_short |
Hotel price forecasting using time series. An exploratory research |
| title_full |
Hotel price forecasting using time series. An exploratory research |
| title_fullStr |
Hotel price forecasting using time series. An exploratory research |
| title_full_unstemmed |
Hotel price forecasting using time series. An exploratory research |
| title_sort |
Hotel price forecasting using time series. An exploratory research |
| dc.creator.none.fl_str_mv |
Chávez-Miranda, Esther Toral, Sergio Martínez-Torres, M. Rocío |
| author |
Chávez-Miranda, Esther |
| author_facet |
Chávez-Miranda, Esther Toral, Sergio Martínez-Torres, M. Rocío |
| author_role |
author |
| author2 |
Toral, Sergio Martínez-Torres, M. Rocío |
| author2_role |
author author |
| dc.contributor.none.fl_str_mv |
Ministerio de Ciencia, Innovación y Universidades European Commission Repositorio Institucional de la Universitat Politècnica de València Riunet |
| dc.subject.none.fl_str_mv |
Hotel price Time series Autoregressive models Revenue management |
| topic |
Hotel price Time series Autoregressive models Revenue management |
| description |
[EN] This paper proposes the use of time-series-based forecasting methods to identify the main predictor variables of prices in hotels located in the city of Barcelona. However, in contrast to previous work, the research focusses on online prices, i.e. the prices set by hotel companies' revenue management algorithms, rather than purchase prices. For the training of the time series, a dataset of hotel prices offered on from Booking.com with a horizon of zero days in advance has been used. In addition to the price series itself, a set of exogenous variables has been included to improve the predictive capacity of the model. As a result, the relative importance of the lags of the endogenous variables and of the exogenous variables, as well as the prediction error, have been obtained. The lag is the main variable in the determination of the forecast and, more specifically, those referring to one day-, one week-, and one month-lags. |
| publishDate |
2023 |
| dc.date.none.fl_str_mv |
2023 2023-09-22 |
| dc.type.none.fl_str_mv |
book part http://purl.org/coar/resource_type/c_3248 VoR http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| dc.type.openaire.fl_str_mv |
info:eu-repo/semantics/bookPart |
| format |
bookPart |
| dc.identifier.none.fl_str_mv |
https://riunet.upv.es/handle/10251/201701 |
| url |
https://riunet.upv.es/handle/10251/201701 |
| dc.language.none.fl_str_mv |
Inglés eng |
| language_invalid_str_mv |
Inglés |
| language |
eng |
| dc.relation.none.fl_str_mv |
Ministerio de Ciencia e Innovación http://dx.doi.org/10.13039/501100004837 |
| dc.rights.none.fl_str_mv |
open access http://purl.org/coar/access_right/c_abf2 Reconocimiento - No comercial - Compartir igual (by-nc-sa) http://creativecommons.org/licenses/by-nc-sa/4.0/ |
| dc.rights.openaire.fl_str_mv |
info:eu-repo/semantics/openAccess |
| rights_invalid_str_mv |
open access http://purl.org/coar/access_right/c_abf2 Reconocimiento - No comercial - Compartir igual (by-nc-sa) http://creativecommons.org/licenses/by-nc-sa/4.0/ |
| eu_rights_str_mv |
openAccess |
| dc.format.none.fl_str_mv |
application/pdf |
| dc.publisher.none.fl_str_mv |
Editorial Universitat Politècnica de València |
| publisher.none.fl_str_mv |
Editorial Universitat Politècnica de València |
| dc.source.none.fl_str_mv |
reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia instname:Universitat Politècnica de València (UPV) |
| instname_str |
Universitat Politècnica de València (UPV) |
| reponame_str |
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| collection |
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| repository.name.fl_str_mv |
|
| repository.mail.fl_str_mv |
|
| _version_ |
1869417350481051648 |
| score |
15.300719 |