Hotel sales forecasting with LSTM and N-BEATS
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Tarih
2023-09-15
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE
Erişim Hakkı
info:eu-repo/semantics/closedAccess
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.
Açıklama
Anahtar Kelimeler
LSTM, N-BEATS, RNN, Time-series, Tourism, Energy utilization, Forecasting, Global warming, Hotels, Learning systems, Long short-term memory, Polynomials, Regression analysis, Water levels, Datapoints, Energy-consumption, Long sequences, Sales forecasting, Time series forecasting, Sales
Kaynak
8th International Conference on Computer Science and Engineering (UBMK)
WoS Q Değeri
Scopus Q Değeri
N/A
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Sayı
Künye
Özçelik, 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/UBMK59864.2023.10286597
