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  • Küçük Resim
    Yayın
    Hotel sales forecasting with LSTM and N-BEATS
    (IEEE, 2023-09-15) Özçelik, Şuayb Talha; Tek, Faik Boray; Şekerci, Erdal
    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.
  • Küçük Resim
    Yayın
    Forecasting and analysis of domestic solid waste generation in districts of istanbul with support vector regression
    (Institute of Electrical and Electronics Engineers Inc., 2020-10-12) Özçelik, Şuayb Talha; Tek, Faik Boray
    Waste planning is essential for large and developing cities such as Istanbul. In this report, we perform data analysis on "Waste Amount Based on District, Year and Waste Type"dataset shared by Istanbul Metropolitan Municipality. After analyzing the waste of the districts, we used support vector regression (SVR) to forecast the waste amounts for the coming years. The analysis has shown an overall increasing trend in the waste generation, although it dropped in 2019. The SVR predicts that the most waste generating district will be Küçükçekmece in the coming years.