Hotel sales forecasting with LSTM and N-BEATS

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dc.authorid0000-0003-3903-7356
dc.authorid0000-0002-8649-6013
dc.authorid0000-0001-9300-5748
dc.contributor.authorÖzçelik, Şuayb Talhaen_US
dc.contributor.authorTek, Faik Borayen_US
dc.contributor.authorŞekerci, Erdalen_US
dc.date.accessioned2023-11-29T14:44:24Z
dc.date.available2023-11-29T14:44:24Z
dc.date.issued2023-09-15
dc.departmentIşık Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering and Natural Sciences, Department of Computer Engineeringen_US
dc.description.abstractTime series forecasting aims to model the change in data points over time. It is applicable in many areas, such as energy consumption, solid waste generation, economic indicators (inflation, currency), global warming (heat, water level), and hotel sales forecasting. This paper focuses on hotel sales forecasting with machine learning and deep learning solutions. A simple forecast solution is to repeat the last observation (Naive method) or the average of the past observations (Average method). More sophisticated solutions have been developed over the years, such as machine learning methods that have linear (Linear Regression, ARIMA) and nonlinear (Polynomial Regression and Support Vector Regression) methods. Different kinds of neural networks are developed and used in time series forecasting problems, and two of the successful ones are Recurrent Neural Networks and N-BEATS. This paper presents a forecasting analysis of hotel sales from Türkiye and Cyprus. We showed that N-BEATS is a solid choice against LSTM, especially in long sequences. Moreover, N-BEATS has slightly better inference time results in long sequences, but LSTM is faster in short sequences.en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationÖzçelik, Ş. T., Tek, F. B. & Şekerci, E. (2023). Hotel sales forecasting with LSTM and N-BEATS. Paper presendted at the 8th International Conference on Computer Science and Engineering (UBMK), 584-589. doi:10.1109/UBMK59864.2023.10286597en_US
dc.identifier.doi10.1109/UBMK59864.2023.10286597
dc.identifier.endpage589
dc.identifier.isbn9798350340815
dc.identifier.isbn9798350340822
dc.identifier.issn2521-1641
dc.identifier.issn2768-0592
dc.identifier.scopus2-s2.0-85177605987
dc.identifier.scopusqualityN/A
dc.identifier.startpage584
dc.identifier.urihttps://hdl.handle.net/11729/5802
dc.identifier.urihttp://dx.doi.org/10.1109/UBMK59864.2023.10286597
dc.indekslendigikaynakScopusen_US
dc.institutionauthorÖzçelik, Şuayb Talhaen_US
dc.institutionauthorid0000-0003-3903-7356
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherIEEEen_US
dc.relation.ispartof8th International Conference on Computer Science and Engineering (UBMK)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectLSTMen_US
dc.subjectN-BEATSen_US
dc.subjectRNNen_US
dc.subjectTime-seriesen_US
dc.subjectTourismen_US
dc.subjectEnergy utilizationen_US
dc.subjectForecastingen_US
dc.subjectGlobal warmingen_US
dc.subjectHotelsen_US
dc.subjectLearning systemsen_US
dc.subjectLong short-term memoryen_US
dc.subjectPolynomialsen_US
dc.subjectRegression analysisen_US
dc.subjectWater levelsen_US
dc.subjectDatapointsen_US
dc.subjectEnergy-consumptionen_US
dc.subjectLong sequencesen_US
dc.subjectSales forecastingen_US
dc.subjectTime series forecastingen_US
dc.subjectSalesen_US
dc.titleHotel sales forecasting with LSTM and N-BEATSen_US
dc.typeConference Objecten_US

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