3 sonuçlar
Arama Sonuçları
Listeleniyor 1 - 3 / 3
Yayın Hotel sales forecasting with LSTM and N-BEATS(IEEE, 2023-09-15) Özçelik, Şuayb Talha; Tek, Faik Boray; Şekerci, ErdalTime 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.Yayın The emergence of projected scaled patterns of extreme temperatures over Europe(Frontiers Media SA, 2023-06-28) Öztürk, Tuğba; Canbaz, Emine; Bilgin, Başak; Matte, Dominic; Kurnaz, Mehmet Levent; Christensen, Jens HesselbjergThis work investigates the scalability of extreme temperatures over the European domain with global warming levels. We have used the EURO-CORDEX ensemble of regional model simulations at 0.11° resolution for daily minimum and maximum temperatures to analyze future changes in extreme weather daily events. Scaling with the annual mean global warming modeled by the driving GCM was applied to future extreme temperature indices changes. Regional changes in each index were scaled by corresponding global warming levels obtained from GCMs. This approach asserts that regional patterns of climate change and average global temperature change are linearly related. It can provide information regarding climate change for periods or emission scenarios when no simulations exist. According to the results, the annual minimum of the lowest temperature of the day (TNn) increases more than the annual maximum of the highest temperature of the day (TXx) for Europe. The multi-model mean of the changes in scaled patterns of extreme temperatures emerges early, around 2020, even before it becomes robust. Individual scaled patterns of TNn and TXx emerge from around 2040.Yayın Evaluation of acceleration characteristics on operational eco - driving(Işık Üniversitesi, 2020-06-16) Yıldıran, Çağlar Latif; Kesten, Ali Sercan; Işık Üniversitesi, Fen Bilimleri Enstitüsü, İnşaat Mühendisliği Yüksek Lisans ProgramıGreenhouse gasses is a main thread for global warming and there are several strategies that reducing GHG gasses especially in developing countries as using alternative fuel types, dealing with the congestion, maintaining the steady traffic flow, dealing with the maintenances of vehicle, managing desired speed and the acceleration rates and so on. Eco-Driving is indicating any implementations which enables driving more economic and ecologic style. One of the main concepts of eco-driving is configuring driving behaviour to reduce consumption and emissions. In this thesis, impact of driver's behaviour tried to be investigated by using an instantaneous emission modelling to obtain minimum acceleration deceleration rate in generically created urban network.
