Hotel Sales Forecasting with LSTM and N-BEATS

Suayb Talha Ozcelik, Faik Boray Tek, Erdal Sekerci

Araştırma sonucu: ???type-name???Konferans katkısıbilirkişi

Özet

Time 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.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıUBMK 2023 - Proceedings
Ana bilgisayar yayını alt yazısı8th International Conference on Computer Science and Engineering
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
Sayfalar584-589
Sayfa sayısı6
ISBN (Elektronik)9798350340815
DOI'lar
Yayın durumuYayınlandı - 2023
Etkinlik8th International Conference on Computer Science and Engineering, UBMK 2023 - Burdur, Turkey
Süre: 13 Eyl 202315 Eyl 2023

Yayın serisi

AdıUBMK 2023 - Proceedings: 8th International Conference on Computer Science and Engineering

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???event.eventtypes.event.conference???8th International Conference on Computer Science and Engineering, UBMK 2023
Ülke/BölgeTurkey
ŞehirBurdur
Periyot13/09/2315/09/23

Bibliyografik not

Publisher Copyright:
© 2023 IEEE.

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